Inductive logic programming for relational knowledge discovery

Inductive logic programming (ILP) is concerned with the induction of logic programs from examples and background knowledge. In ILP, the shift of attention from program synthesis to knowledge discovery resulted in advanced techniques that are practically applicable for discovering knowledge in relational databases. This paper gives a brief introduction to ILP, presents selected ILP techniques for relational knowledge discovery and reviews selected ILP applications. Nada Lavrač, Ph.D.: She is a senior research associate at the Department of Intelligent Systems, J. Stefan Institute, Ljubljana, Slovenia (since 1978) and a visiting professor at the Klagenfurt University, Austria (since 1987). Her main research interest is in machine learning, in particular inductive logic programming and intelligent data analysis in medicine. She received a BSc in Technical Mathematics and MSc in Computer Science from Ljubljana University, and a PhD in Technical Sciences from Maribor University, Slovenia. She is coauthor of KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems, The MIT Press 1989, and Inductive Logic Programming: Techniques and Applications, Ellis Horwood 1994, and coeditor of Intelligent Data Analysis in Medicine and Pharmacology, Kluwer 1997. She was the coordinator of the European Scientific Network in Inductive Logic Programming ILPNET (1993–1996) and program cochair of the 8th European Machine Learning Conference ECML’95, and 7th International Workshop on Inductive Logic Programming ILP’97. Sašo Džeroski, Ph.D.: He is a research associate at the Department of Intelligent Systems, J. Stefan Institute, Ljubljana, Slovenia (since 1989). He has held visiting researcher positions at the Turing Institute, Glasgow (UK), Katholieke Universiteit Leuven (Belgium), German National Research Center for Computer Science (GMD), Sankt Augustin (Germany) and the Foundation for Research and Technology-Hellas (FORTH), Heraklion (Greece). His research interest is in machine learning and knowledge discovery in databases, in particular inductive logic programming and its applications and knowledge discovery in environmental databases. He is co-author of Inductive Logic Programming: Techniques and Applications, Ellis Horwood 1994. He is the scientific coordinator of ILPnet2, The Network of Excellence in Inductive Logic Programming. He was program co-chair of the 7th International Workshop on Inductive Logic Programming ILP’97 and will be program co-chair of the 16th International Conference on Machine Learning ICML’99. Masayuki Numao, Ph.D.: He is an associate professor at the Department of Computer Science, Tokyo Institute of Technology. He received a bachelor of engineering in electrical and electronics engineering in 1982 and his Ph.D. in computer science in 1987 from Tokyo Institute of Technology. He was a visiting scholar at CSLI, Stanford University from 1989 to 1990. His research interests include Artificial Intelligence, Global Intelligence and Machine Learning. Numao is a member of Information Processing Society of Japan, Japanese Society for Artificial Intelligence, Japanese Cognitive Science Society, Japan Society for Software Science and Technology and AAAI.     

Privacy-preserving SVM classification

Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What is required is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid results, while providing guarantees on the nondisclosure of data. Support vector machine classification is one of the most widely used classification methodologies in data mining and machine learning. It is based on solid theoretical foundations and has wide practical application. This paper proposes a privacy-preserving solution for support vector machine (SVM) classification, PP-SVM for short. Our solution constructs the global SVM classification model from data distributed at multiple parties, without disclosing the data of each party to others. Solutions are sketched out for data that is vertically, horizontally, or even arbitrarily partitioned. We quantify the security and efficiency of the proposed method, and highlight future challenges. Jaideep Vaidya received the Bachelor’s degree in Computer Engineering from the University of Mumbai. He received the Master’s and the Ph.D. degrees in Computer Science from Purdue University. He is an Assistant Professor in the Management Science and Information Systems Department at Rutgers University. His research interests include data mining and analysis, information security, and privacy. He has received best paper awards for papers in ICDE and SIDKDD. He is a Member of the IEEE Computer Society and the ACM. Hwanjo Yu received the Ph.D. degree in Computer Science in 2004 from the University of Illinois at Urbana-Champaign. He is an Assistant Professor in the Department of Computer Science at the University of Iowa. His research interests include data mining, machine learning, database, and information systems. He is an Associate Editor of Neurocomputing and served on the NSF Panel in 2006. He has served on the program committees of 2005 ACM SAC on Data Mining track, 2005 and 2006 IEEE ICDM, 2006 ACM CIKM, and 2006 SIAM Data Mining. Xiaoqian Jiang received the B.S. degree in Computer Science from Shanghai Maritime University, Shanghai, 2003. He received the M.C.S. degree in Computer Science from the University of Iowa, Iowa City, 2005. Currently, he is pursuing a Ph.D. degree from the School of Computer Science, Carnegie Mellon University. His research interests are computer vision, machine learning, data mining, and privacy protection technologies.     

Relative positioning for team robot navigation

The research presented in this paper approaches the issue of robot team navigation using relative positioning. With this approach each robot is equipped with sensors that allow it to independently estimate the relative direction of an assigned leader. Acoustic sensor systems are used and were seen to work very effectively in environments where datum relative positioning systems (such as GPS or acoustic transponders) are typically ineffective. While acoustic sensors provide distinct advantages, the variability of the acoustic environment presents significant control challenges. To address this challenge, directional control of the robot was accomplished with a feed forward neural network trained using a genetic algorithm, and a new approach to training using recent memories was successfully implemented. The design of this controller is presented and its performance is compared with more traditional classic logic and behavior controllers. Patrick McDowell received his bachelor's degree in Computer Science in 1984 from the University of Idaho. He spent the next 15 years working as a computer scientist for a small defense contractor where he specialized in real time data acquisition, application development, and image processing. In 1999 he received his master's degree in computer science from the University of Southern Mississippi. In 2000 he began work at the Naval Research Lab where he has focused on application of machine learning techniques to autonomous underwater navigation. In 2005 he received his Ph.D. in Computer Science from Louisiana State University. His research interests include legged robotics, machine learning, and artificial intelligence. In Fall of 2006 he joined Southeastern Louisiana University as an assistant professor of Computer Science. Brian S. Bourgeois received his Ph.D. in Electrical Engineering from Tulane University located in New Orleans, LA in 1991. Since then he has worked at the Stennis Space Center, MS detachment of the Naval Research Laboratory. He has worked on research projects spanning an array of technologies including airborne survey sytems, acoustic backscattering, bathymetry and imaging sonar systems, the ORCA unmanned underwater vehicle and the development of an autonomous survey system for hydrographic survey ships. He is presently the head of the Position, Navigation and Timing team at NRL with research interests including underwater positioning and communications and autonomous navigation. Ms. McDowell received her M.S. in Applied Physics in 2002 from the University or New Orleans. She is presently a candidate for a Ph. D. in Engineering and Applied Science. She joined the Naval Research Laboratory in 1991 as a research engineer and has spent most of that time working in experimental and theoretical acoustic modeling. Ms. McDowell's specific research interest lie in the areas of sonar performance analysis. Dr. S. S. Iyengar is the Chairman and Roy Paul Daniels Chaired Professor of Computer Science at Louisiana State University and is also Satish Dhawan Chaired Professor at Indian Institute of Science. He has been involved with research in high-performance algorithms, data structures, sensor fusion, data mining, and intelligent systems since receiving his Ph.D. degree (1974) and his M.S. from the Indian Institute of Science (1970). He has been a consultant to several industrial and government organizations (JPL, NASA etc.). In 1999, Professor Iyengar won the most prestigious research award titled Distinguished Research Award and a university medal for his research contributions in optimal algorithms for sensor fusion/image processing. Dr. Jianhua Chen received her Ph.D. in computer science in 1988 from Jilin University, Chang Chun, China. In August 1988, She joined the Computer Science Department of Louisiana State University, Baton Rouge, USA, where she is currently an associate professor. Dr. Chen's research interests include Machine Learning and Data Mining, Fuzzy Sets and Systems, Knowledge Representation and Reasoning.     

The pairwise attribute noise detection algorithm

Analyzing the quality of data prior to constructing data mining models is emerging as an important issue. Algorithms for identifying noise in a given data set can provide a good measure of data quality. Considerable attention has been devoted to detecting class noise or labeling errors. In contrast, limited research work has been devoted to detecting instances with attribute noise, in part due to the difficulty of the problem. We present a novel approach for detecting instances with attribute noise and demonstrate its usefulness with case studies using two different real-world software measurement data sets. Our approach, called Pairwise Attribute Noise Detection Algorithm (PANDA), is compared with a nearest neighbor, distance-based outlier detection technique (denoted DM) investigated in related literature. Since what constitutes noise is domain specific, our case studies uses a software engineering expert to inspect the instances identified by the two approaches to determine whether they actually contain noise. It is shown that PANDA provides better noise detection performance than the DM algorithm. Jason Van Hulse is a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. His research interests include data mining and knowledge discovery, machine learning, computational intelligence and statistics. He is a student member of the IEEE and IEEE Computer Society. He received the M.A. degree in mathematics from Stony Brook University in 2000, and is currently Director, Decision Science at First Data Corporation. Taghi M. Khoshgoftaar is a professor at the Department of Computer Science and Engineering, Florida Atlantic University, and the director of the Empirical Software Engineering and Data Mining and Machine Learning Laboratories. His research interests are in software engineering, software metrics, software reliability and quality engineering, computational intelligence, computer performance evaluation, data mining, machine learning, and statistical modeling. He has published more than 300 refereed papers in these subjects. He has been a principal investigator and project leader in a number of projects with industry, government, and other research-sponsoring agencies. He is a member of the IEEE, the IEEE Computer Society, and IEEE Reliability Society. He served as the program chair and general chair of the IEEE International Conference on Tools with Artificial Intelligence in 2004 and 2005, respectively. Also, he has served on technical program committees of various international conferences, symposia, and workshops. He has served as North American editor of the Software Quality Journal, and is on the editorial boards of the journals Empirical Software Engineering, Software Quality, and Fuzzy Systems. Haiying Huang received the M.S. degree in computer engineeringfrom Florida Atlantic University, Boca Raton, Florida, USA, in 2002. She is currently a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. Her research interests include software engineering, computational intelligence, data mining, software measurement, software reliability, and quality engineering.     

Learning Horn definitions: Theory and an application to planning

A Horn definition is a set of Horn clauses with the same predicate in all head literals. In this paper, we consider learning non-recursive, first-order Horn definitions from entailment. We show that this class is exactly learnable from equivalence and membership queries. It follows then that this class is PAC learnable using examples and membership queries. Finally, we apply our results to learning control knowledge for efficient planning in the form of goal-decomposition rules. Chandra Reddy, Ph.D.: He is currently a doctoral student in the Department of Computer Science at Oregon State University. He is completing his Ph.D. on June 30, 1998. His dissertation is entitled “Learning Hierarchical Decomposition Rules for Planning: An Inductive Logic Programming Approach.” Earlier, he had an M. Tech in Artificial Intelligence and Robotics from University of Hyderabad, India, and an M.Sc.(tech) in Computer Science from Birla Institute of Technology and Science, India. His current research interests broadly fall under machine learning and planning/scheduling—more specifically, inductive logic programming, speedup learning, data mining, and hierarchical planning and optimization. Prasad Tadepalli, Ph.D.: He has an M.Tech in Computer Science from Indian Institute of Technology, Madras, India and a Ph.D. from Rutgers University, New Brunswick, USA. He joined Oregon State University, Corvallis, as an assistant professor in 1989. He is now an associate professor in the Department of Computer Science of Oregon State University. His main area of research is machine learning, including reinforcement learning, inductive logic programming, and computational learning theory, with applications to classification, planning, scheduling, manufacturing, and information retrieval.     

Geo-located image categorization and location recognition

Image categorization is undoubtedly one of the most recent and challenging problems faced in Computer Vision. The scientific literature is plenty of methods more or less efficient and dedicated to a specific class of images; further, commercial systems are also going to be advertised in the market. Nowadays, additional data can also be attached to the images, enriching its semantic interpretation beyond the pure appearance. This is the case of geo-location data that contain information about the geographical place where an image has been acquired. This data allow, if not require, a different management of the images, for instance, to the purpose of easy retrieval from a repository, or of identifying the geographical place of an unknown picture, given a geo-referenced image repository. This paper constitutes a first step in this sense, presenting a method for geo-referenced image categorization, and for the recognition of the geographical location of an image without such information available. The solutions presented are based on robust pattern recognition techniques, such as the probabilistic Latent Semantic Analysis, the Mean Shift clustering and the Support Vector Machines. Experiments have been carried out on a couple of geographical image databases: results are actually very promising, opening new interesting challenges and applications in this research field. The article is published in the original. Marco Cristani received the Laurea degree in 2002 and the Ph.D. degree in 2006, both in Computer Science from the University of Verona, Verona, Italy. He was a visiting Ph.D. student at the Computer Vision Lab, Institute for Robotics and Intelligent Systems School of Engineering (IRIS), University of Southern California, Los Angeles, in 2004–2005. He is now an Assistant Professor with the Department of Computer Science, University of Verona, working with the Vision, Image Processing and Sounds (VIPS) Lab. His main research interests include statistical pattern recognition, generative modeling via graphical models, and non-parametric data fusion techniques, with applications on surveillance, segmentation and image and video retrieval. He is the author of several papers in the above subjects and a reviewer for several international conferences and journals. Alessandro Perina received the BD and MS degrees in Information Technologies and Intelligent and Multimedia Systems from the University of Verona, Verona, Italy, in 2004 and 2006, respectively. He is currently a Ph.D. candidate in the Computer Science Department at the University of Verona. His research interests include computer vision, machine learning and pattern recognition. He is a student member of the IEEE. Umberto Castellani is Ricercatore (i.e., Research Assistant) of Department of Computer Science at University of Verona. He received his Dottorato di Ricerca (Ph.D.) in Computer Science from the University of Verona in 2003 working on 3D data modelling and reconstruction. During his Ph.D., he had been Visiting Research Fellow at the Machine Vision Unit of the Edinburgh University, in 2001. In 2007 he has been an Invited Professor for two months at the LASMEA laboratory in Clermont-Ferrand, France. In 2008, he has been Visiting Researcher for two months at the PRIP laboratory of the Michigan State University (USA). His main research interests concern the processing of 3D data coming from different acquisition systems such as 3D models from 3D scanners, acoustic images for the vision in underwater environment, and MRI scans for biomedical applications. The addressed methodologies are focused on the intersections among Machine Learning, Computer Vision and Computer Graphics. Vittorio Murino received the Laurea degree in electronic engineering in 1989 and the Ph.D. degree in electronic engineering and computer science in 1993, both from the University of Genoa, Genoa, Italy. He is a Full Professor with the Department of Computer Science, University of Verona. From 1993 to 1995, he was a Postdoctoral Fellow in the Signal Processing and Understanding Group, Department of Biophysical and electronic Engineering, University of Genoa, where he supervised of research activities on image processing for object recognition and pattern classification in underwater environments. From 1995 to 1998, he was an Assistant Professor of the Department of Mathematics and Computer Science, University of Udine, Udine, Italy. Since 1998, he has been with the University of Verona, where he founded and is responsible for the Vision, Image processing, and Sound (VIPS) Laboratory. He is scientifically responsible for several national and European projects and is an Evaluator for the European Commission of research project proposals related to different scientific programmes and frameworks. His main research interests include computer vision and pattern recognition, probabilistic techniques for image and video processing, and methods for integrating graphics and vision. He is author or co-author of more than 150 papers published in refereed journals and international conferences. Dr. Murino is a referee for several international journals, a member of the technical committees for several conferences (ECCV, ICPR, ICIP), and a member of the editorial board of Pattern Recognition, IEEE Transactions on Systems, Man, and Cybernetics, Pattern Analysis and Applications and Electronic Letters on Computer Vision and Image Analysis (ELCVIA). He was the promotor and Guest Editor off our special issues of Pattern Recognition and is a Fellow of the IAPR.     

Discovering validation rules from microbiological data

A huge amount of data is daily collected from clinical microbiology laboratories. These data concern the resistance or susceptibility of bacteria to tested antibiotics. Almost all microbiology laboratories follow standard antibiotic testing guidelines which suggest antibiotic test execution methods and result interpretation and validation (among them, those annually published by NCCLS^2,3). Guidelines basically specify, for each species, the antibiotics to be tested, how to interpret the results of tests and a list of exceptions regarding particular antibiotic test results. Even if these standards are quite assessed, they do not consider peculiar features of a given hospital laboratory, which possibly influence the antimicrobial test results, and the further validation process. In order to improve and better tailor the validation process, we have applied knowledge discovery techniques, and data mining in particular, to microbiological data with the purpose of discovering new validation rules, not yet included in NCCLS guidelines, but considered plausible and correct by interviewed experts. In particular, we applied the knowledge discovery process in order to find (association) rules relating to each other the susceptibility or resistance of a bacterium to different antibiotics. This approach is not antithetic, but complementary to that based on NCCLS rules: it proved very effective in validating some of them, and also in extending that compendium. In this respect, the new discovered knowledge has lead microbiologists to be aware of new correlations among some antimicrobial test results, which were previously unnoticed. Last but not least, the new discovered rules, taking into account the history of the considered laboratory, are better tailored to the hospital situation, and this is very important since some resistances to antibiotics are specific to particular, local hospital environments. Evelina Lamma, Ph.D.: She got her degree in Electrical Engineering at the University of Bologna in 1985, and her Ph.D. in Computer Science in 1990. Her research activity centers on logic programming languages, artificial intelligence and agent-based programming. She was co-organizers of the 3rd International Workshop on Extensions of Logic Programming ELP92, held in Bologna in February 1992, and of the 6th Italian Congress on Artificial Intelligence, held in Bologna in September 1999. She is a member of the Italian Association for Artificial Intelligence (AI^*IA), associated with ECCAI. Currently, she is Full Professor at the University of Ferrara, where she teaches Artificial Intelligence and Fondations of Computer Science. Fabrizio Riguzzi, Ph.D.: He is Assistant Professor at the Department of Engineering of the University of Ferrara, Italy. He received his Laurea from the University of Bologna in 1999. He joined the Department of Engineering of the University of Ferrara in 1999. He has been a visiting researcher at the University of Cyprus and at the New University of Lisbon. His research interests include: data mining (and in particular methods for learning from multirelational data), machine learning, belief revision, genetic algorithms and software engineering. Sergio Storari: He got his degree in Electrical Engineering at the University of Ferrara in 1998. His research activity centers on artificial intelligence, knowledge-based systems, data mining and multi-agent systems. He is a member of the Italian Association for Artificial Intelligence (AI^*IA), associated with ECCAI. Currently, he is attending the third year of Ph.D. course about “Study and application of Artificial Intelligence techniques for medical data analysis” at DEIS University of Bologna. Paola Mello, Ph.D.: She got her degree in Electrical Engineering at the University of Bologna in 1982, and her Ph.D. in Computer Science in 1988. Her research activity centers on knowledge representation, logic programming, artificial intelligence and knowledge-based systems. She was co-organizers of the 3rd International Workshop on Extensions of Logic Programming ELP92, held in Bologna in February 1992, and of the 6th Italian Congress on Artificial Intelligence, Held in Bologna in September 1999. She is a member of the Italian Association for Artificial Intelligence (AI^*IA), associated with ECCAI. Currently, she is Full Professor at the University of Bologna, where she teaches Artificial Intelligence and Fondations of Computer Science. Anna Nanetti: She got a degree in biologics sciences at the University of Bologna in 1974. Currently, she is an Academic Recearcher in the Microbiology section of the Clinical, Specialist and Experimental Medicine Department of the Faculty of Medicine and Surgery, University of Bologna.     

Merging uncertain information with semantic heterogeneity in XML

Semistructured information can be merged in a logic-based framework [6, 7]. This framework has been extended to deal with uncertainty, in the form of probability values, degrees of beliefs, or necessity measures, associated with leaves (i.e. textentries) in the XML documents [3]. In this paper we further extend this approach to modelling and merging uncertain information that is defined at different levels of granularity of XML textentries, and to modelling and reasoning with XML documents that contain semantically heterogeneous uncertain information on more complex elements in XML subtrees. We present the formal definitions for modelling, propagating and merging semantically heterogeneous uncertain information and explain how they can be handled using logic-based fusion techniques. Anthony Hunter received a B.Sc. (1984) from the University of Bristol and an M.Sc. (1987) and Ph.D. (1992) from Imperial College, London. He is currently a reader in the Department of Computer Science at University College London. His main research interests are: Knowledge representation and reasoning, Analysing inconsistency, Argumentation, Default reasoning and Knowledge Fusion. Weiru Liu is a senior lecturer at the School of Computer Science, Queen's University Belfast. She received her B.Sc. and M.Sc. degrees in Computer Science from Jilin University, P.R China, and her Ph.D. degree in Artificial Intelligence from the University of Edinburgh. Her main research interests include reasoning under uncertainty, knowledge representation and reasoning, uncertain knowledge and information fusion, and knowledge discovery in databases. She has published over 50 journal and conference papers in these areas.     

A study of the model and algorithms for handling location-dependent continuous queries

Advances in wireless and mobile computing environments allow a mobile user to access a wide range of applications. For example, mobile users may want to retrieve data about unfamiliar places or local life styles related to their location. These queries are called location-dependent queries. Furthermore, a mobile user may be interested in getting the query results repeatedly, which is called location-dependent continuous querying. This continuous query emanating from a mobile user may retrieve information from a single-zone (single-ZQ) or from multiple neighbouring zones (multiple-ZQ). We consider the problem of handling location-dependent continuous queries with the main emphasis on reducing communication costs and making sure that the user gets correct current-query result. The key contributions of this paper include: (1) Proposing a hierarchical database framework (tree architecture and supporting continuous query algorithm) for handling location-dependent continuous queries. (2) Analysing the flexibility of this framework for handling queries related to single-ZQ or multiple-ZQ and propose intelligent selective placement of location-dependent databases. (3) Proposing an intelligent selective replication algorithm to facilitate time- and space-efficient processing of location-dependent continuous queries retrieving single-ZQ information. (4) Demonstrating, using simulation, the significance of our intelligent selective placement and selective replication model in terms of communication cost and storage constraints, considering various types of queries. Manish Gupta received his B.E. degree in Electrical Engineering from Govindram Sakseria Institute of Technology & Sciences, India, in 1997 and his M.S. degree in Computer Science from University of Texas at Dallas in 2002. He is currently working toward his Ph.D. degree in the Department of Computer Science at University of Texas at Dallas. His current research focuses on AI-based software synthesis and testing. His other research interests include mobile computing, aspect-oriented programming and model checking. Manghui Tu received a Bachelor degree of Science from Wuhan University, P.R. China, in 1996, and a Master's Degree in Computer Science from the University of Texas at Dallas 2001. He is currently working toward the Ph.D. degree in the Department of Computer Science at the University of Texas at Dallas. Mr. Tu's research interests include distributed systems, wireless communications, mobile computing, and reliability and performance analysis. His Ph.D. research work focuses on the dependent and secure data replication and placement issues in network-centric systems. Latifur R. Khan has been an Assistant Professor of Computer Science department at University of Texas at Dallas since September 2000. He received his Ph.D. and M.S. degrees in Computer Science from University of Southern California (USC) in August 2000 and December 1996, respectively. He obtained his B.Sc. degree in Computer Science and Engineering from Bangladesh University of Engineering and Technology, Dhaka, Bangladesh, in November of 1993. Professor Khan is currently supported by grants from the National Science Foundation (NSF), Texas Instruments, Alcatel, USA, and has been awarded the Sun Equipment Grant. Dr. Khan has more than 50 articles, book chapters and conference papers focusing in the areas of database systems, multimedia information management and data mining in bio-informatics and intrusion detection. Professor Khan has also served as a referee for database journals, conferences (e.g. IEEE TKDE, KAIS, ADL, VLDB) and he is currently serving as a program committee member for the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD2005), ACM 14th Conference on Information and Knowledge Management (CIKM 2005), International Conference on Database and Expert Systems Applications DEXA 2005 and International Conference on Cooperative Information Systems (CoopIS 2005), and is program chair of ACM SIGKDD International Workshop on Multimedia Data Mining, 2004. Farokh Bastani received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology, Bombay, and the M.S. and Ph.D. degrees in Computer Science from the University of California, Berkeley. He is currently a Professor of Computer Science at the University of Texas at Dallas. Dr. Bastani's research interests include various aspects of the ultrahigh dependable systems, especially automated software synthesis and testing, embedded real-time process-control and telecommunications systems and high-assurance systems engineering. Dr. Bastani was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (IEEE-TKDE). He is currently an emeritus EIC of IEEE-TKDE and is on the editorial board of the International Journal of Artificial Intelligence Tools, the International Journal of Knowledge and Information Systems and the Springer-Verlag series on Knowledge and Information Management. He was the program cochair of the 1997 IEEE Symposium on Reliable Distributed Systems, 1998 IEEE International Symposium on Software Reliability Engineering, 1999 IEEE Knowledge and Data Engineering Workshop, 1999 International Symposium on Autonomous Decentralised Systems, and the program chair of the 1995 IEEE International Conference on Tools with Artificial Intelligence. He has been on the program and steering committees of several conferences and workshops and on the editorial boards of the IEEE Transactions on Software Engineering, IEEE Transactions on Knowledge and Data Engineering and the Oxford University Press High Integrity Systems Journal. I-Ling Yen received her B.S. degree from Tsing-Hua University, Taiwan, and her M.S. and Ph.D. degrees in Computer Science from the University of Houston. She is currently an Associate Professor of Computer Science at University of Texas at Dallas. Dr. Yen's research interests include fault-tolerant computing, security systems and algorithms, distributed systems, Internet technologies, E-commerce and self-stabilising systems. She has published over 100 technical papers in these research areas and received many research awards from NSF, DOD, NASA and several industry companies. She has served as Program Committee member for many conferences and Program Chair/Cochair for the IEEE Symposium on Application-Specific Software and System Engineering & Technology, IEEE High Assurance Systems Engineering Symposium, IEEE International Computer Software and Applications Conference, and IEEE International Symposium on Autonomous Decentralized Systems. She has also served as a guest editor for a theme issue of IEEE Computer devoted to high-assurance systems.     

Evolutionary fusion of a multi-classifier system for efficient face recognition

In this paper an evolutionary classifier fusion method inspired by biological evolution is presented to optimize the performance of a face recognition system. Initially, different illumination environments are modeled as multiple contexts using unsupervised learning and then the optimized classifier ensemble is searched for each context using a Genetic Algorithm (GA). For each context, multiple optimized classifiers are searched; each of which are referred to as a context based classifier. An evolutionary framework comprised of a combination of these classifiers is then applied to optimize face recognition as a whole. Evolutionary classifier fusion is compared with the simple adaptive system. Experiments are carried out using the Inha database and FERET database. Experimental results show that the proposed evolutionary classifier fusion method gives superior performance over other methods without using evolutionary fusion. Recommended by Guest Editor Daniel Howard. This work was supported by INHA UNIVERSITY Research Grant. Zhan Yu received the B.E. degree in Software Engineering from Xiamen University, China, in 2008. He is currently a master student in Intelligent Technology Lab, Computer and Information Department, Inha University, Korea. He has research interests in image processing, pattern recognition, computer vision, machine learning and statistical inference and computating. Mi Young Nam received the B.Sc. and M.Sc. degrees in Computer Science from the University of Silla Busan, Korea in 1995 and 2001 respectively and the Ph.D. degree in Computer Science & Engineering from the University of Inha, Korea in 2006. Currently, She is Post-Doctor course in Intelligent Technology Laboratory, Inha University, Korea. She’s research interest includes biometrics, pattern recognition, computer vision, image processing. Suman Sedai received the M.S. degree in Software Engineering from Inha University, China, in 2008. He is currently a Doctoral course in Western Australia University, Australia. He has research interests in image processing, pattern recognition, computer vision, machine learning. Phill Kyu Rhee received the B.S. degree in Electrical Engineering from the Seoul University, Seoul, Korea, the M.S. degree in Computer Science from the East Texas State University, Commerce, TX, and the Ph.D. degree in Computer Science from the University of Louisiana, Lafayette, LA, in 1982, 1986, and 1990 respectively. During 1982–1985 he was working in the System Engineering Research Institute, Seoul, Korea as a research scientist. In 1991 he joined the Electronic and Telecommunication Research Institute, Seoul, Korea, as a Senior Research Staff. Since 1992, he has been an Associate Professor in the Department of Computer Science and Engineering of the Inha University, Incheon, Korea and since 2001, he is a Professor in the same department and university. His current research interests are pattern recognition, machine intelligence, and parallel computer architecture. dr. rhee is a Member of the IEEE Computer Society and KISS (Korea Information Science Society).     

Learning with globally predictive tests

We introduce a new bias for rule learning systems. The bias only allows a rule learner to create a rule that predicts class membership if each test of the rule in isolation is predictive of that class. Although the primary motivation for the bias is to improve the understandability of rules, we show that it also improves the accuracy of learned models on a number of problems. We also introduce a related preference bias that allows creating rules that violate this restriction if they are statistically significantly better than alternative rules without such violations. Michael J. Pazzani, Ph.D.: He is a Full Professor and Chair in the Department of Information and Computer Science at the University of California, Irvine. He obtained his bachelors degree from the University of Connecticut in 1980 and his Ph. D. from University of California, Los Angles in 1987. His research interests are in machine learning, cognitive modeling and information access. He has published over 100 research papers and 2 books. He has served on the Editorial Board of the Machine Learning and the Journal of Artificial Intelligence Research.     

Dynamic vehicle routing using genetic algorithms

Many difficult combinatorial optimization problems have been modeled as static problems. However, in practice, many problems are dynamic and changing, while some decisions have to be made before all the design data are known. For example, in the Dynamic Vehicle Routing Problem (DVRP), new customer orders appear over time, and new routes must be reconfigured while executing the current solution. Montemanni et al. [1] considered a DVRP as an extension to the standard vehicle routing problem (VRP) by decomposing a DVRP as a sequence of static VRPs, and then solving them with an ant colony system (ACS) algorithm. This paper presents a genetic algorithm (GA) methodology for providing solutions for the DVRP model employed in [1]. The effectiveness of the proposed GA is evaluated using a set of benchmarks found in the literature. Compared with a tabu search approach implemented herein and the aforementioned ACS, the proposed GA methodology performs better in minimizing travel costs. Franklin T. Hanshar is currently a M.Sc. student in the Department of Computing and Information Science at the University of Guelph, Ontario, Canada. He received a B.Sc. degree in Computer Science from Brock University in 2005. His research interests include uncertain reasoning, optimization and evolutionary computation. Beatrice Ombuki-Berman is currently an Associate Professor in the Department of Computer Science at Brock University, Ontario, Canada. She obtained a PhD and ME in Information Engineering from University of The Ryukyus, Okinawa, Japan in 2001 and 1998, respectively. She received a B.Sc. in Mathematics and Computer Science from Jomo Kenyatta University, Nairobi, Kenya. Her primary research interest is evolutionary computation and applied optimization. Other research interests include neural networks, machine learning and ant colony optimization.     

A program specialiser for meta-level compositions of logic programs

Metal-level compositions of object logic programs are naturally implemented by means of meta-programming techniques. Metainterpreters defining program compositions however suffer from a computational overhead that is due partly to the interpretation layer present in all meta-programs, and partly to the specific interpretation layer needed to deal with program compositions. We show that meta-interpreters implementing compositions of object programs can be fruitfully specialised w.r.t. meta-level queries of the form Demo (E, G), where E denotes a program expression and G denotes a (partially instantiated) object level query. More precisely, we describe the design and implementation of declarative program specialiser that suitably transforms such meta-interpreters so as to sensibly reduce — if not to completely remove — the overhead due to the handling of program compositions. In many cases the specialiser succeeds in eliminating also the overhead due to meta-interpretation. Antonio Brogi, Ph.D.: He is currently assistant professor in the Department of Computer Science at the University of Pisa, Italy. He received his Laurea Degree in Computer Science (1987) and his Ph. D. in Computer Science (1993) from the University of Pisa. His research interests include programming language design and semantics, logic programming, deductive databases, and software coordination. Simone Contiero: He is currently a Ph. D. student at the Department of Computer Science, University of Pisa (Italy). He received his Laurea Degree in Computer Science from the University of Pisa in 1994. His research interests are in high-level programming languages, metaprogramming and logic-based coordination of software.     

Applying language modeling to session identification from database trace logs

A database session is a sequence of requests presented to the database system by a user or an application to achieve a certain task. Session identification is an important step in discovering useful patterns from database trace logs. The discovered patterns can be used to improve the performance of database systems by prefetching predicted queries, rewriting the current query or conducting effective cache replacement.In this paper, we present an application of a new session identification method based on statistical language modeling to database trace logs. Several problems of the language modeling based method are revealed in the application, which include how to select values for the parameters of the language model, how to evaluate the accuracy of the session identification result and how to learn a language model without well-labeled training data. All of these issues are important in the successful application of the language modeling based method for session identification. We propose solutions to these open issues. In particular, new methods for determining an entropy threshold and the order of the language model are proposed. New performance measures are presented to better evaluate the accuracy of the identified sessions. Furthermore, three types of learning methods, namely, learning from labeled data, learning from semi-labeled data and learning from unlabeled data, are introduced to learn language models from different types of training data. Finally, we report experimental results that show the effectiveness of the language model based method for identifying sessions from the trace logs of an OLTP database application and the TPC-C Benchmark. Xiangji Huang joined York University as an Assistant Professor in July 2003 and then became a tenured Associate Professor in May 2006. Previously, he was a Post Doctoral Fellow at the School of Computer Science, University of Waterloo, Canada. He did his Ph.D. in Information Science at City University in London, England, with Professor Stephen E. Robertson. Before he went into his Ph.D. program, he worked as a lecturer for 4 years at Wuhan University. He also worked in the financial industry in Canada doing E-business, where he was awarded a CIO Achievement Award, for three and half years. He has published more than 50 refereed papers in journals, book chapter and conference proceedings. His Master (M.Eng.) and Bachelor (B.Eng.) degrees were in Computer Organization & Architecture and Computer Engineering, respectively. His research interests include information retrieval, data mining, natural language processing, bioinformatics and computational linguistics. Qingsong Yao is a Ph.D. student in the Department of Computer Science and Engineering at York University, Toronto, Canada. His research interests include database management systems and query optimization, data mining, information retrieval, natural language processing and computational linguistics. He earned his Master's degree in Computer Science from Institute of Software, Chinese Academy of Science in 1999 and Bachelor's degree in Computer Science from Tsinghua University. Aijun An is an associate professor in the Department of Computer Science and Engineering at York University, Toronto, Canada. She received her Bachelor's and Master's degrees in Computer Science from Xidian University in China. She received her PhD degree in Computer Science from the University of Regina in Canada in 1997. She worked at the University of Waterloo as a postdoctoral fellow from 1997 to 1999 and as a research assistant professor from 1999 to 2001. She joined York University in 2001. She has published more than 60 papers in refereed journals and conference proceedings. Her research interests include data mining, machine learning, and information retrieval.     

Effective classification of noisy data streams with attribute-oriented dynamic classifier selection

Recently, mining from data streams has become an important and challenging task for many real-world applications such as credit card fraud protection and sensor networking. One popular solution is to separate stream data into chunks, learn a base classifier from each chunk, and then integrate all base classifiers for effective classification. In this paper, we propose a new dynamic classifier selection (DCS) mechanism to integrate base classifiers for effective mining from data streams. The proposed algorithm dynamically selects a single “best” classifier to classify each test instance at run time. Our scheme uses statistical information from attribute values, and uses each attribute to partition the evaluation set into disjoint subsets, followed by a procedure that evaluates the classification accuracy of each base classifier on these subsets. Given a test instance, its attribute values determine the subsets that the similar instances in the evaluation set have constructed, and the classifier with the highest classification accuracy on those subsets is selected to classify the test instance. Experimental results and comparative studies demonstrate the efficiency and efficacy of our method. Such a DCS scheme appears to be promising in mining data streams with dramatic concept drifting or with a significant amount of noise, where the base classifiers are likely conflictive or have low confidence. A preliminary version of this paper was published in the Proceedings of the 4th IEEE International Conference on Data Mining, pp 305–312, Brighton, UK Xingquan Zhu received his Ph.D. degree in Computer Science from Fudan University, Shanghai, China, in 2001. He spent four months with Microsoft Research Asia, Beijing, China, where he was working on content-based image retrieval with relevance feedback. From 2001 to 2002, he was a Postdoctoral Associate in the Department of Computer Science, Purdue University, West Lafayette, IN. He is currently a Research Assistant Professor in the Department of Computer Science, University of Vermont, Burlington, VT. His research interests include Data mining, machine learning, data quality, multimedia computing, and information retrieval. Since 2000, Dr. Zhu has published extensively, including over 40 refereed papers in various journals and conference proceedings. Xindong Wu is a Professor and the Chair of the Department of Computer Science at the University of Vermont. He holds a Ph.D. in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published extensively in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, IJCAI, ICML, KDD, ICDM, and WWW, as well as 11 books and conference proceedings. Dr. Wu is the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (by the IEEE Computer Society), the founder and current Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), an Honorary Editor-in-Chief of Knowledge and Information Systems (by Springer), and a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He is the 2004 ACM SIGKDD Service Award winner. Ying Yang received her Ph.D. in Computer Science from Monash University, Australia in 2003. Following academic appointments at the University of Vermont, USA, she currently holds a Research Fellow at Monash University, Australia. Dr. Yang is recognized for contributions in the fields of machine learning and data mining. She has published many scientific papers and book chapters on adaptive learning, proactive mining, noise cleansing and discretization. Contact her at yyang@mail.csse.monash.edu.au.     

A Framework for the Generation of Adaptive Courses Based on Semantic Metadata

This approach proposes the creation and management of adaptive learning systems by combining component technology, semantic metadata, and adaptation rules. A component model allows interaction among components that share consistent assumptions about what each provides and each requires of the other. It allows indexing, using, reusing, and coupling of components in different contexts powering adaptation. Our claim is that semantic metadata are required to allow a real reusing and assembling of educational component. Finally, a rule language is used to define strategies to rewrite user query and user model. The former allows searching components developing concepts not appearing in the user query but related with user goals, whereas the last allow inferring user knowledge that is not explicit in user model.John Freddy Duitama received his M.Sc. degree in system engineering from the University of Antioquia -Colombia (South America). He is currently a doctoral candidate in the GET – Institut National des Télécommunications, Evry France. This work is sponsored by the University of Antioquia, where he is assistant professor.His research interest includes semantic web and web-based learning systems, educational metadata and learning objects.Bruno Defude received his Ph.D. in Computer Science from the University of Grenoble (I.N.P.G) in 1986. He is currently Professor in the Department of Computer Science at the GET - Institut National des Télécommunications, Evry France where he leads the SIMBAD project (Semantic Interoperability for MoBile and ADaptive applications).His major field of research interest is databases and semantic web, specifically personalized data access, adaptive systems, metadata, interoperability and semantic Peer-to-peer systems with elearning as a privileged application area.He is a member of ACM SIGMOD.Amel Bouzeghoub received a degree of Ph.D. in Computer Sciences at Pierre et Marie Curie University, France.In 2000, she joined the Computer Sciences Department of GET-INT (Institut National des Telecommunications) at Evry (France) as an associate professor.Her research interests include topics related to Web-based Learning Systems, Semantic Metadata for learning resources, Adaptive Learning Systems and Intelligent Tutoring Systems.Claire Lecocq received an Engineer Degree and a Ph.D. in Computer Sciences respectively in 1994 and 1999. In 1997, she joined the Computer Sciences Department at GET-INT (Institut National des Télécommunications) of Evry, France, as an associate professor. Her first research interests included spatial databases and visual query languages. She is now working on adaptive learning systems, particularly on semantic metadata and user models.     

Supervised tensor learning

Tensor representation is helpful to reduce the small sample size problem in discriminative subspace selection. As pointed by this paper, this is mainly because the structure information of objects in computer vision research is a reasonable constraint to reduce the number of unknown parameters used to represent a learning model. Therefore, we apply this information to the vector-based learning and generalize the vector-based learning to the tensor-based learning as the supervised tensor learning (STL) framework, which accepts tensors as input. To obtain the solution of STL, the alternating projection optimization procedure is developed. The STL framework is a combination of the convex optimization and the operations in multilinear algebra. The tensor representation helps reduce the overfitting problem in vector-based learning. Based on STL and its alternating projection optimization procedure, we generalize support vector machines, minimax probability machine, Fisher discriminant analysis, and distance metric learning, to support tensor machines, tensor minimax probability machine, tensor Fisher discriminant analysis, and the multiple distance metrics learning, respectively. We also study the iterative procedure for feature extraction within STL. To examine the effectiveness of STL, we implement the tensor minimax probability machine for image classification. By comparing with minimax probability machine, the tensor version reduces the overfitting problem. We focus on the convex optimization-based binary classification learning algorithms in this paper. This is because the solution to a convex optimization-based learning algorithm is unique. Dacheng Tao received the B.Eng. degree from the University of Science and Technology of China (USTC), the MPhil degree from the Chinese University of Hong Kong (CUHK) and the PhD from the University of London (Birkbeck). He will join the Department of Computing in the Hong Kong Polytechnic University as an assistant professor. His research interests include biometric research, discriminant analysis, support vector machine, convex optimization for machine learning, multilinear algebra, multimedia information retrieval, data mining, and video surveillance. He published extensively at TPAMI, TKDE, TIP, TMM, TCSVT, CVPR, ICDM, ICASSP, ICIP, ICME, ACM Multimedia, ACM KDD, etc. He gained several Meritorious Awards from the Int’l Interdisciplinary Contest in Modeling, which is the highest level mathematical modeling contest in the world, organized by COMAP. He is a guest editor for special issues of the Int’l Journal of Image and Graphics (World Scientific) and the Neurocomputing (Elsevier). Xuelong Li works at the University of London. He has published in journals (IEEE T-PAMI, T-CSVT, T-IP, T-KDE, TMM, etc.) and conferences (IEEE CVPR, ICASSP, ICDM, etc.). He is an Associate Editor of IEEE T-SMC, Part C, Neurocomputing, IJIG (World Scientific), and Pattern Recognition (Elsevier). He is also an Editor Board Member of IJITDM (World Scientific) and ELCVIA (CVC Press). He is a Guest Editor for special issues of IJCM (Taylor and Francis), IJIG (World Scientific), and Neurocomputing (Elsevier). He co-chaired the 5th Annual UK Workshop on Computational Intelligence and the 6th the IEEE Int’l Conf. on Machine Learning and Cybernetics. He was also a publicity chair of the 7th IEEE Int’l Conf. on Data Mining and the 4th Int’l Conf. on Image and Graphics. He has been on the program committees of more than 50 conferences and workshops. Xindong Wu is a Professor and the Chair of the Department of Computer Science at the University of Vermont. He holds a Ph.D. in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published extensively in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, IJCAI, AAAI, ICML, KDD, ICDM, and WWW, as well as 12 books and conference proceedings. Dr. Wu is the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (by the IEEE Computer Society), the Founder and current Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), an Honorary Editor-in-Chief of Knowledge and Information Systems (by Springer), and a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AIKP). He is the 2004 ACM SIGKDD Service Award winner. Weiming Hu received the Ph.D. degree from the Department of Computer Science and Engineering, Zhejiang University. From April 1998 to March 2000, he was a Postdoctoral Research Fellow with the Institute of Computer Science and Technology, Founder Research and Design Center, Peking University. Since April 1998, he has been with the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. Now he is a Professor and a Ph.D. Student Supervisor in the laboratory. His research interests are in visual surveillance, neural networks, filtering of Internet objectionable information, retrieval of multimedia, and understanding of Internet behaviors. He has published more than 80 papers on national and international journals, and international conferences. Stephen J. Maybank received a BA in Mathematics from King’s college, Cambridge in 1976 and a PhD in Computer Science from Birkbeck College, University of London in 1988. He was a research scientist at GEC from 1980 to 1995, first at MCCS, Frimley and then, from 1989, at the GEC Marconi Hirst Research Centre in London. In 1995 he became a lecturer in the Department of Computer Science at the University of Reading and in 2004 he became a professor in the School of Computer Science and Information Systems at Birkbeck College, University of London. His research interests include camera calibration, visual surveillance, tracking, filtering, applications of projective geometry to computer vision and applications of probability, statistics and information theory to computer vision. He is the author of more than 90 scientific publications and one book. He is a Fellow of the Institute of Mathematics and its Applications, a Fellow of the Royal Statistical Society and a Senior Member of the IEEE. For further information see http://www.dcs.bbk.ac.uk/~sjmaybank.     

Pruning boosted classifiers with a real valued genetic algorithm

Ensemble classifiers and algorithms for learning ensembles have recently received a great deal of attention in the machine learning literature (R.E. Schapire, Machine Learning 5(2) (1990) 197–227;N. Cesa-Bianchi, Y. Freund, D. Haussler, D.P. Helbold, R.E. Schapire, M.K. Warmuth, Proceedings of the 25th Annual ACM Symposium on the Theory of Computing, 1993, pp. 382–391; L. Breiman, Bias, Technical Report 460, Statistics Department, University of California, Berkeley, CA, 1996; J.R. Quinlan, Proceedings of the 14th International Conference on Machine Learning, Italy, 1997; Y. Freund, R.E. Schapire, Proceedings of the 13th International Conference on Machine Learning ICML96, Bari, Italy 1996, pp. 148–157; A.J.C. Sharkey, N.E. Sharkey, Combining diverse neural nets, The Knowledge Engineering Review 12 (3) (1997) 231–247). In particular, boosting has received a great deal of attention as a mechanism by which an ensemble of classifiers that has a better generalisation characteristic than any single classifier derived using a particular technique can be discovered. In this article, we examine and compare a number of techniques for pruning a classifier ensemble which is overfit on its training set and find that a real valued GA is at least as good as the best heuristic search algorithm for choosing an ensemble weighting.     

Automated Problem Determination Using Call-Stack Matching

We present an architecture and algorithms for performing automated software problem determination using call-stack matching. In an environment where software is used by a large user community, the same problem may re-occur many times. We show that this can be detected by matching the program call-stack against a historical database of call-stacks, so that as soon as the problem has been resolved once, future cases of the same or similar problems can be automatically resolved. This would greatly reduce the number of cases that need to be dealt with by human support analysts. We also show how a call-stack matching algorithm can be automatically learned from a small sample of call-stacks labeled by human analysts, and examine the performance of this learning algorithm on two different data sets.Mark Brodie is a research staff member in the “Machine Learning for Systems” group at the IBM T.J. Watson Research Center in Hawthorne, NY. He did his undergraduate work in Mathematics at the University of the Witwatersrand in South Africa and received his PhD in Computer Science at the University of Illinois in 2000. His research interests include machine learning, data mining, and problem determination.Sheng Ma received his BS degree in Electrical Engineering from Tsinghua University, Beijing China, in 1992, and his MS and PhD with honors in Electrical Engineering from Rensselaer Polytechnic Institute, Troy, NY, in 1995 and 1998, respectively. He joined the IBM T.J. Watson Research Center as a research staff member in 1998 and became manager of the “Machine Learning for Systems” group in 2001. His current research interests include machine learning, data mining, network traffic modeling and control, and network and computer systems management.Leonid Rachevsky is a software systems analyst in the “Machine Learning for Systems” group at the IBM T.J. Watson Research Center in Hawthorne, NY. He obtained his MSc in Mathematics at Kazan State University and his PhD in Technical Science (Applied Mathematics) at the Kazan Institute of Chemical Engineering in Kazan, USSR (now Russia). He has worked extensively as a software engineer and senior software analyst in Israel, Canada and the United States.Jon Champlin is an advisory software engineer with the Lotus division of IBM’s Software Group. He received a Bachelors of Computer Science from Siena College in 1993. He is part of the external support group and has developed several serviceability features for Lotus Notes/Domino.