Composite of adaptive support vector regression and nonlinear conditional heteroscedasticity tuned by quantum minimization for forecasts

Adaptive support vector regression (ASVR) applied to the forecast of complex time series is superior to the other traditional prediction methods. However, the effect of volatility clustering occurred in time-series actually deteriorates ASVR prediction accuracy. Therefore, incorporating nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) model into ASVR is employed for dealing with the problem of volatility clustering to best fit the forecast’s system. Interestingly, quantum-based minimization algorithm is proposed in this study to tune the resulting coefficients between ASVR and NGARCH, in such a way that the ASVR/NGARCH composite model can achieve the best accuracy of prediction. Quantum optimization here tackles so-called NP-completeness problem and outperforms the real-coded genetic algorithm on the same problem because it accomplishes better approach to the optimal or near-optimal coefficient-found. It follows that the proposed method definitely obtains the satisfactory results because of highly balancing generalization and localization for composite model and thus improving forecast accuracy. Bao Rong Chang is currently an Associate Professor in the Department of Computer Science and Information Engineering at National Taitung University in Taitung, Taiwan. He completed his BS degree from the Department of Electronic Engineering, Tam Kang University, Taiwan. In 1990, he earned his ME degree from the Department of Electrical Engineering, University of Missouri-Columbia, USA, and his Ph.D. in 1994 at the same University. His current research interests include Intelligent Computations, Applied Computer Network, and Financial Engineering. Hsiu-Fen Tsai is currently a Senior Lecturer in the Department of International Business at Shu Te University in Kaohsiung, Taiwan. She completed her BA degree from the Department of International Business, National Taiwan University, Taiwan. In 1995, she earned her MBA degree from the Department of Business Administration, National Taiwan University, Taiwan. At present, she is a Ph. D. Candidate in Department of International Business since 2004 at the same University. Her current research interests include Intelligent Analysis of Business Models and Applications of Strategy Management.     

A Fast Scalable Classifier Tightly Integrated with RDBMS

In this pager,we report our success in building efficient scalable classifiers by exploring the capabilities of modern relational database management systems (RDBMS).In addition to high classification accuracy,the unique features of the approach include its high training speed ,linear scalability,and simplicity in implementation.More importantly,the major computation required in the approach can be implemented using standard functions provided by the modern realtional DBMS.Besides,with the effective rule pruning strategy,the algorithm proposed in this paper can produce a compact set of classification rules,The results of experiments conducted for performance evaluation an analysis are presented.     

Ranking-based evaluation of regression models

We suggest the use of ranking-based evaluation measures for regression models, as a complement to the commonly used residual-based evaluation. We argue that in some cases, such as the case study we present, ranking can be the main underlying goal in building a regression model, and ranking performance is the correct evaluation metric. However, even when ranking is not the contextually correct performance metric, the measures we explore still have significant advantages: They are robust against extreme outliers in the evaluation set; and they are interpretable. The two measures we consider correspond closely to non-parametric correlation coefficients commonly used in data analysis (Spearman's ρ and Kendall's τ); and they both have interesting graphical representations, which, similarly to ROC curves, offer useful various model performance views, in addition to a one-number summary in the area under the curve. An interesting extension which we explore is to evaluate models on their performance in “partially” ranking the data, which we argue can better represent the utility of the model in many cases. We illustrate our methods on a case study of evaluating IT Wallet size estimation models for IBM's customers. Saharon Rosset is Research Staff Member in the Data Analytics Research Group at IBM's T. J. Watson Research Center. He received his B.S. in Mathematics and M.Sc., in Statistics from Tel Aviv University in Israel, and his Ph.D. in Statistics from Stanford University in 2003. In his research, he aspires to develop practically useful predictive modeling methodologies and tools, and apply them to solve problems in business and scientific domains. Currently, his major projects include work on customer wallet estimation and analysis of genetic data. Claudia Perlich has received a M.Sc. in Computer Science from Colorado University at Boulder, a Diploma in Computer Science from Technische Universitaet in Darmstadt, and her Ph.D. in Information Systems from Stern School of Business, New York University. Her Ph.D. thesis concentrated on probability estimation in multi-relational domains that capture information of multiple entity types and relationships between them. Her dissertation was recognized as an additional winner of the International SAP Doctoral Support Award Competition. Claudia joined the Data Analytics Research group at IBM's T.J. Watson Research Center as a Research Staff Member in October 2004. Her research interests are in statistical machine learning for complex real-world domains and business applications. Bianca Zadrozny is currently an associate professor at the Computer Science Department of Federal Fluminense University in Brazil. Her research interests are in the areas of applied machine learning and data mining. She received her B.Sc. in Computer Engineering from the Pontifical Catholic University in Rio de Janeiro, Brazil, and her M.Sc. and Ph.D. in Computer Science from the University of California at San Diego. She has also worked as a research staff member in the data analytics research group at IBM T.J. Watson Research Center.     

Deterioration of visual information in face classification using Eigenfaces and Fisherfaces

In the area of biometrics, face classification becomes one of the most appealing and commonly used approaches for personal identification. There has been an ongoing quest for designing systems that exhibit high classification rates and portray significant robustness. This feature becomes of paramount relevance when dealing with noisy and uncertain images. The design of face recognition classifiers capable of operating in presence of deteriorated (noise affected) face images requires a careful quantification of deterioration of the existing approaches vis-à-vis anticipated form and levels of image distortion. The objective of this experimental study is to reveal some general relationships characterizing the performance of two commonly used face classifiers (that is Eigenfaces and Fisherfaces) in presence of deteriorated visual information. The findings obtained in our study are crucial to identify at which levels of noise the face classifiers can still be considered valid. Prior knowledge helps us develop adequate face recognition systems. We investigate several typical models of image distortion such as Gaussian noise, salt and pepper, and blurring effect and demonstrate their impact on the performance of the two main types of the classifiers. Several distance models derived from the Minkowski family of distances are investigated with respect to the produced classification rates. The experimental environment concerns a well-known standard in this area of face biometrics such as the FERET database. The study reports on the performance of the classifiers, which is based on a comprehensive suite of experiments and delivers several design hints supporting further developments of face classifiers. Gabriel Jarillo Alvarado obtained his B.Sc. degree in Biomedical Engineering from the Universidad Iberoamericana, Mexico. In 2003 he obtained his M.Sc. degree from the University of Alberta at the Department of Electrical and Computer Engineering, he is currently enrolled in the Ph.D. program at the same University. His research interests involve machine learning, pattern recognition, and evolutionary computation with particular interest to biometrics for personal identification. Witold Pedrycz is a Professor and Canada Research Chair (CRC) in Computational Intelligence) in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. His research interests involve Computational Intelligence, fuzzy modeling, knowledge discovery and data mining, fuzzy control including fuzzy controllers, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published numerous papers in this area. He is also an author of 9 research monographs. Witold Pedrycz has been a member of numerous program committees of conferences in the area of fuzzy sets and neurocomputing. He currently serves on editorial board of numereous journals including IEEE Transactions on Systems Man and Cybernetics, Pattern Recognition Letters, IEEE Transactions on Fuzzy Systems, Fuzzy Sets & Systems, and IEEE Transactions on Neural Networks. He is an Editor-in-Chief of Information Sciences. Marek Reformat received his M.Sc. degree from Technical University of Poznan, Poland, and his Ph.D. from University of Manitoba, Canada. His interests were related to simulation and modeling in time-domain, as well as evolutionary computing and its application to optimization problems For three years he worked for the Manitoba HVDC Research Centre, Canada, where he was a member of a simulation software development team. Currently, Marek Reformat is with the Department of Electrical and Computer Engineering at University of Alberta. His research interests lay in the areas of application of Computational Intelligence techniques, such as neuro-fuzzy systems and evolutionary computing, as well as probabilistic and evidence theories to intelligent data analysis leading to translating data into knowledge. He applies these methods to conduct research in the areas of Software and Knowledge Engineering. He has been a member of program committees of several conferences related to Computational Intelligence and evolutionary computing. Keun-Chang Kwak received B.Sc., M.Sc., and Ph.D. degrees in the Department of Electrical Engineering from Chungbuk National University, Cheongju, South Korea, in 1996, 1998, and 2002, respectively. During 2002–2003, he worked as a researcher in the Brain Korea 21 Project Group, Chungbuk National University. His research interests include biometrics, computational intelligence, pattern recognition, and intelligent control.     

A proposal for a set of attributes relevant for Web portal data quality

Data Quality is a critical issue in today’s interconnected society. Advances in technology are making the use of the Internet an ever-growing phenomenon and we are witnessing the creation of a great variety of applications such as Web Portals. These applications are important data sources and/or means of accessing information which many people use to make decisions or to carry out tasks. Quality is a very important factor in any software product and also in data. As quality is a wide concept, quality models are usually used to assess the quality of a software product. From the software point of view there is a widely accepted standard proposed by ISO/IEC (the ISO/IEC 9126) which proposes a quality model for software products. However, until now a similar proposal for data quality has not existed. Although we have found some proposals of data quality models, some of them working as “de facto” standards, none of them focus specifically on web portal data quality and the user’s perspective. In this paper, we propose a set of 33 attributes which are relevant for portal data quality. These have been obtained from a revision of literature and a validation process carried out by means of a survey. Although these attributes do not conform to a usable model, we think that it might be considered as a good starting point for constructing one.

Building measure-based prediction models for UML class diagram maintainability

The usefulness of measures for the analysis and design of object oriented (OO) software is increasingly being recognized in the field of software engineering research. In particular, recognition of the need for early indicators of external quality attributes is increasing. We investigate through experimentation whether a collection of UML class diagram measures could be good predictors of two main subcharacteristics of the maintainability of class diagrams: understandability and modifiability. Results obtained from a controlled experiment and a replica support the idea that useful prediction models for class diagrams understandability and modifiability can be built on the basis of early measures, in particular, measures that capture structural complexity through associations and generalizations. Moreover, these measures seem to be correlated with the subjective perception of the subjects about the complexity of the diagrams. This fact shows, to some extent, that the objective measures capture the same aspects as the subjective ones. However, despite our encouraging findings, further empirical studies, especially using data taken from real projects performed in industrial settings, are needed. Such further study will yield a comprehensive body of knowledge and experience about building prediction models for understandability and modifiability.

Chance discovery and scenario analysis

Scenario analysis is often used to identify possible chance events. However, no formal, computational theory yet exists for scenario analysis. In this paper, we commence development of such a theory by defining a scenario in an argumentation context, and by considering the question of when two scenarios are the same. Peter McBurney, Ph.D.: He is a lecturer in the Department of Computer Science at the University of Liverpool, UK. He has a first degree in Pure Mathematics and Statistics from the Australian National University, Canberra, and a Ph.D in Artificial Intelligence from the University of Liverpool. His Ph.D research concerned the design of protocols for rational interaction between autonomous software agents, and he has several publications in this area. Prior to completing his Ph.D he worked as a consultant to major telecommunications network operating companies, primarily in mobile and satellite communications, where his work involved strategic marketing programming. Simon Parsons, Ph.D.: He is currently visiting the Sloan School of Management at Massachusetts Institute of Technology (MIT) and is a Visiting Professor at the University of Liverpool, UK. He holds a first degree in Engineering from Cambridge University, and an MSc and Ph.D in Artificial Intelligence from the University of London. In 1998, he was awarded the Young Engineer Achievement Medal of the British Institution of Electrical Engineers (IEE), the largest professional engineering society in Europe. He has published 4 books and over 100 articles on autonomous agents and multi-agent systems, uncertainty formalisms, risk and decision-making.     

A framework for protein structure prediction on the grid

The large number of protein sequences, provided by genomic projects at an increasing pace, constitutes a challenge for large scale computational studies of protein structure and thermodynamics. Grid technology is very suitable to face this challenge, since it provides a way to access the resources needed in compute and data intensive applications. In this paper, we show the procedure to adapt to the Grid an algorithm for the prediction of protein thermodynamics, using the GridWay tool. GridWay allows the resolution of large computational experiments by reacting to events dynamically generated by both the Grid and the application. Eduardo Huedo, Ph.D.: He is a Computer Engineer (1999) and Ph.D. in Computer Architecture (2004) by the Universidad Complutense de Madrid (UCM). He is Scientist in the Advanced Computing Laboratory at Centro de Astrobiología (CSIC-INTA), associated to NASA Astrobiology Institute. He had one appointment in 2000 as a Summer Student in High Performance Computing and Applied Mathematics at ICASE (NASA Langley Research Center). His research areas are Performance Management and Tuning, High Performance Computing and Grid Technology. Ugo Bastolla, Ph.D.: He received his degree and Ph.D. in Physics in Rome University, with L. Peliti and G. Parisi respectively. He was interested from the beginning in biologically motivated problems, therefore, studied models of Population Genetics, Boolean Networks, Neural Networks, Statistical Mechanics of Polymers, Ecological and Biodiversity. His main research interest is constituted by studies of protein folding thermodynamics and evolution. Thereby, he set up an effective energy function allowing prediction of protein folding thermodynamics, and applied it to protein structure prediction, to simulate protein evolution and to analyze protein sequences from a thermodynamical point of view. He is currently in the Bioinformatic Unit of the Centro de Astrobiología of Madrid. Rubén S. Montero, Ph.D.: He received his B.S. in Physics (1996), M.S in Computer Science (1998) and Ph.D. in Computer Architecture (2002) from the Universidad Complutense de Madrid (UCM). He is Assistant Professor of Computer Architecture and Technology at UCM since 1999. He has held several research appointments at ICASE (NASA Langley Research Center), where he worked on computational fluid dynamics, parallel multigrid algorithms and Cluster computing. Nowadays, his research interests lie mainly in Grid Technology, in particular in adaptive scheduling, adaptive execution and distributed algorithms. Ignacio M. Llorente, Ph.D.: He received his B.S. in Physics (1990), M.S in Computer Science (1992) and Ph.D. in Computer Architecture (1995) from the Universidad Complutense de Madrid (UCM). He is Executive M.B.A. by Instituto de Empresa since 2003. He is Associate Professor of Computer Architecture and Technology in the Department of Computer Architecture and System Engineering at UCM and Senior Scientist at Centro de Astrobiología (CSIC-INTA), associated to NASA Astrobiology Institute. He has held several appointments since 1997 as a Consultant in High Performance Computing and Applied Mathematics at ICASE (NASA Langley Research Center). His research areas are Information Security, High Performance Computing and Grid Technology.     

Automatic outlier detection for time series: an application to sensor data

In this article we consider the problem of detecting unusual values or outliers from time series data where the process by which the data are created is difficult to model. The main consideration is the fact that data closer in time are more correlated to each other than those farther apart. We propose two variations of a method that uses the median from a neighborhood of a data point and a threshold value to compare the difference between the median and the observed data value. Both variations of the method are fast and can be used for data streams that occur in quick succession such as sensor data on an airplane. Martin Meckesheimer has been a member of the Applied Statistics Group at Phantom Works, Boeing since 2001. He received a Bachelor of Science Degree in Industrial Engineering from the University of Pittsburgh in 1997, and a Master's Degree in Industrial and Systems Engineering from Ecole Centrale Paris in 1999. Martin earned a Doctorate in Industrial Engineering from The Pennsylvania State University in August 2001, as a student of Professor Russell R. Barton and Dr. Timothy W. Simpson. His primary research interests are in the areas of design of experiments and surrogate modeling. Sabyasachi Basu received his Ph.D. is Statistics from the University of Wisconsin at Madison in 1990. Since his Ph.D., he has worked in both academia and in industry. He has taught and guided Ph.D. students in the Department of Statistics at the Southern Methodist University. He has also worked as a senior marketing statistician at the J. C. Penney Company. Dr. Basu is also an American Society of Quality certified Six Sigma Black Belt. He is currently an Associate Technical Fellow in Statistics and Data Mining at the Boeing Company. In this capacity, he works as a researcher and a technical consultant within Boeing for data mining, statistics and process improvements. He has published more than 20 papers and technical reports. He has also served as journal referee for several journals, organized conferences and been invited to present at conferences.     

Managing the diffusion of organizational learning behavior

This paper addresses the need for organizations to manage the transformation from traditional hierarchical models to ‘learning organizations.’ We propose a five-stage methodology useful in the diffusion of behaviors associated with organizational learning (OL) theory. The stages of OL diffusion are (1) agenda-setting, (2) matching, (3) restructuring, (4) clarifying, and (5) routinizing. Each stage involves both managerial (structural) or member (cultural) influences on organizational memory (OM). Salient definitions are provided and the OM aspects and deliverables associated with each OL diffusion stage are discussed. This research provides a theoretically-driven approach to help change agents diffuse and realize the potential of OL behavior in the firm.

Direct 3D painting with a metaball-based paintbrush

This paper presents a direct 3D painting algorithm for polygonal models in 3D object-space with a metaball-based paintbrush in virtual environment.The user is allowed to directly manipulate the parameters used to shade the surface of the 3D shape by applying the pigment to its surface with direct 3D manipulation through a 3D flying mouse.     

I can’t get no satisfaction: How bundling and multi-part pricing can satisfy consumers and suppliers

Bundling and multi-part pricing may save etailers from mortal challenges attacking the music industry. These strategies are attractive to customers, perhaps spelling the difference between pirating and legally purchasing music; they allow “custom pricing” to capture more of the consumer surplus, and just as importantly, they contribute to developing new artists for long-term viability of the music industry. The many ways to bundle include exact firm-selected bundles, category bundling, customer-selected bundles, and mixing these with individual products. Each of these approaches has specific advantages for different market segments, making up for generally lower prices in the competitive online world. Multi-part pricing affords additional opportunities to capture more of the consumer surplus. These ideas are especially relevant to online music because of the ease of packaging products, the low cost of reproducing music on demand, the reduced friction of consumer/firm interaction, the low cost of monitoring complex behavior, and the enhanced measurement of performance. In the online world, content offerings are revitalized when offered as bundles or service packages. Sam Bodily is the John Tyler Professor of Business Administration at The Darden School, University of Virginia. He has published textbooks and an assortment of practical and scholarly articles in journals ranging from Harvard Business Review to Management Science. Several of his publications relate to perishable-asset revenue management, the stimulation of demand from price-sensitive customers through discount pricing. More generally his publications relate to decision and risk analysis, decision modeling and strategy modeling. He has edited a special issue of Interfaces on Strategy Modeling and Analysis. Prof. Bodily teaches a first-year MBA course in decision analysis, and has a successful second year elective Management Decision Models, and has taught eStrategy and Strategy. He is a past winner of the Decision Sciences International Instructional Award. He has taught numerous executive education programs in strategy, risk analysis, and financial decision analysis for Darden and private companies around the world. Before joining the Darden School faculty, Prof. Bodily was on the faculties of MIT Sloan School of Management and Boston University. He has been a visiting professor at INSEAD, Stanford University and the University of Washington. He has Ph.D. and S.M. degrees from Massachusetts Institute of Technology and a B.S. from Brigham Young University. Rafi Mohammed is an economist who holds advanced degrees in economics from the London School of Economics & Political Science (Diploma) and Cornell University (Ph.D.). His fields of specialty include applied microeconomics, business strategy, marketing, and pricing. An article from his dissertation on pricing and bundling in the music industry was published in the top academic strategy/economics journal, the Rand Journal of Economics. Most recently, Rafi was a consultant and thought leader at Monitor Group in its Cambridge, Massachusetts and Santa Monica, California offices. He has led business strategy and marketing projects in the consumer package goods, film, media, high technology, and music industries. He is the lead author of the McGraw-Hill textbook Internet Marketing: Building Advantage in a Networked Economy (second edition, April 2003). This textbook has been adopted by over 150 universities (co-authored with Robert Fisher, Bernie Jaworski, and Gordon Paddison). Concurrent to his position at Monitor Group, Rafi was awarded a Batten Fellowship in Strategy at the Darden School of Business at the University of Virginia. Rafi currently is an economic/strategy consultant and is writing a trade book on pricing and bundling.     

Community based innovation: How to integrate members of virtual communities into new product development

Online consumer groups represent a large pool of product know-how. Hence, they seem to be a promising source of innovation. At present, except for open source software, little is known about how to utilize this know-how for new product development. In this article we explore if and how members of virtual communities can be integrated into new product development. We explain how to identify and access online communities and how to interact with its members in order to get valuable input for new product development. This approach we term “Community Based Innovation.” The Audi case illustrates the applicability of the method and underscores the innovative capability of consumers encountered in virtual communities. Johann Füller is assistant professor in marketing at Innsbruck University School of Management and board member of HYVE AG, a company specialized in virtual customer integration. He received his Ph.D. in business administration at the Innsbruck University School of Management. Johann holds a degree in mechanical engineering, and industrial engineering and management. His research interests are in the field of innovation creation in online communities and in virtual consumer integration into new product development. Michael Bartl is member of the management board at the HYVE AG in Munich, Germany, specialised in Customized Innovation and Product Design. He finished his Ph.D. Thesis in Business Administration at the Otto Beisheim Graduate School of Management (WHU) obtained his Dipl. Kfm. from the Ludwig-Maximilians-University Munich and his B.A. (Hons) from the University of Westminster London. Holger Ernst is professor at WHU—Otto Beisheim School of Management, Vallendar, Germany where he holds the Chair for Technology and Innovation Management. He is currently visiting professor at the Kellogg School of Management, Northwestern University, Evanston, IL, USA. His main research interests are in the fields of technology and innovation management, intellectual property management, new product development and entrepreneurship. He has published in the Journal of Product Innovation Management, International Journal of Management Reviews, Journal of Engineering and Technology Management, IEEE Transactions on Engineering Management, Research Policy and R&D Management. He consults multiple European organizations in the area of technology, patent and innovation management. Hans Muhlbacher is Professor of Business Administration at the Innsbruck University School of Management. He has been President of the European Marketing Academy and currently is the Associate Editor for International Business of the Journal of Business Research. All along his career he has extensively taught internationally at business schools such as ESSEC or universities such as Emory University, Tulane University, or Paris Pantheon-Assass. His main research interests are in the field of strategy formation, branding, and innovation.     

Management and enterprise architecture click: The FAD(E)E framework

Enterprises are living things. They constantly need to be (re-)architected in order to achieve the necessary agility, alignment and integration. This paper gives a high-level overview of how companies can go about doing ‘enterprise architecture’ in the context of both the classic (isolated) enterprise and the Extended Enterprise. By discussing the goals that are pursued in an enterprise architecture effort we reveal some basic requirements that can be put on the process of architecting the enterprise. The relationship between managing and architecting the enterprise is discussed and clarified in the FAD(E)E, the Framework for the Architectural Development of the (Extended) Enterprise. Frank G. Goethals completed his Master studies in economics (option informatics), at the Katholieke Universiteit Leuven, Belgium, in 2000. He is presently researching for a Ph.D. under the theme of `Managing data in the Extended Enterprise'. This research is conducted at the K.U.Leuven under the guidance of professor J. Vandenbulcke, and is financed by SAP Belgium. Frank has a strong interest in coordination and dependency theory and Enterprise Architecture. Monique Snoeck obtained her Ph.D. in May 1995 from The Department of Computer Science of the Katholieke Universiteit Leuven with a thesis that lays the formal foundations of the object-oriented business modelling method MERODE. Since then she has done further research in the area of formal methods for object-oriented conceptual modelling. She now is Full Professor with the Management Information Systems Group of the Faculty of Economics and Applied Economics at the Katholieke Universiteit Leuven in Belgium. She has been involved in several industrial conceptual modelling projects. Her research interests are object oriented conceptual modelling, software architecture and software quality. Wilfried Lemahieu holds a Ph.D. from the Department of Applied Economic Sciences of the Katholieke Universiteit Leuven, Belgium (1999). At present, he is associate professor at the Management Informatics research group of the Faculty of Economics and Applied Economics. His teaching includes Database Management, Data Storage Architectures and Management Informatics. His research interests comprise distributed object architectures and web services, object-relational and object-oriented database systems and hypermedia systems. Jacques A. Vandenbulcke is professor at the Faculty of Economics and Applied Economics of the Katholieke Universiteit Leuven, Belgium. His main research interests are in Database management, Data modelling, and Business Information Systems. He is co-ordinator of the Leuven Institute for Research on Information Systems (LIRIS) and holder of the SAP-chair on ‘Extended enterprise infrastructures’. He is president of ‘Studiecentrum voor Automatische Informatieverwerking (SAI)’, the largest society for computer professionals in Belgium, and co-founder of the ‘Production and Inventory Control Society (PICS)’ in Belgium.     

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.     

Using discriminant analysis for multi-class classification: an experimental investigation

Many supervised machine learning tasks can be cast as multi-class classification problems. Support vector machines (SVMs) excel at binary classification problems, but the elegant theory behind large-margin hyperplane cannot be easily extended to their multi-class counterparts. On the other hand, it was shown that the decision hyperplanes for binary classification obtained by SVMs are equivalent to the solutions obtained by Fisher's linear discriminant on the set of support vectors. Discriminant analysis approaches are well known to learn discriminative feature transformations in the statistical pattern recognition literature and can be easily extend to multi-class cases. The use of discriminant analysis, however, has not been fully experimented in the data mining literature. In this paper, we explore the use of discriminant analysis for multi-class classification problems. We evaluate the performance of discriminant analysis on a large collection of benchmark datasets and investigate its usage in text categorization. Our experiments suggest that discriminant analysis provides a fast, efficient yet accurate alternative for general multi-class classification problems. Tao Li is currently an assistant professor in the School of Computer Science at Florida International University. He received his Ph.D. degree in Computer Science from University of Rochester in 2004. His primary research interests are: data mining, machine learning, bioinformatics, and music information retrieval. Shenghuo Zhu is currently a researcher in NEC Laboratories America, Inc. He received his B.E. from Zhejiang University in 1994, B.E. from Tsinghua University in 1997, and Ph.D degree in Computer Science from University of Rochester in 2003. His primary research interests include information retrieval, machine learning, and data mining. Mitsunori Ogihara received a Ph.D. in Information Sciences at Tokyo Institute of Technology in 1993. He is currently Professor and Chair of the Department of Computer Science at the University of Rochester. His primary research interests are data mining, computational complexity, and molecular computation.     

Empirical studies to assess the understandability of data warehouse schemas using structural metrics

Data warehouses are powerful tools for making better and faster decisions in organizations where information is an asset of primary importance. Due to the complexity of data warehouses, metrics and procedures are required to continuously assure their quality. This article describes an empirical study and a replication aimed at investigating the use of structural metrics as indicators of the understandability, and by extension, the cognitive complexity of data warehouse schemas. More specifically, a four-step analysis is conducted: (1) check if individually and collectively, the considered metrics can be correlated with schema understandability using classical statistical techniques, (2) evaluate whether understandability can be predicted by case similarity using the case-based reasoning technique, (3) determine, for each level of understandability, the subsets of metrics that are important by means of a classification technique, and assess, by means of a probabilistic technique, the degree of participation of each metric in the understandability prediction. The results obtained show that although a linear model is a good approximation of the relation between structure and understandability, the associated coefficients are not significant enough. Additionally, classification analyses reveal respectively that prediction can be achieved by considering structure similarity, that extracted classification rules can be used to estimate the magnitude of understandability, and that some metrics such as the number of fact tables have more impact than others.

Trends in agent-based computational modeling of macroeconomics

Based on the 50 papers surveyed in Reference,²⁾ this paper addresses general research trends in agent-based macroeconomics. On the aspect ofagent engineering, we highlight two major developments: first, the extensive applications of computational intelligence tools in modeling adaptive behavior, and second the grounding of these applications in the cognitive sciences. Shu-Heng Chen, Ph.D.: He is a professor in the Department of Economics of the National Chengchi University. He now serves as the director of the AI-ECON Research Center, National Chengchi University, the editor-in-chief of the forthcoming journal “Fuzzy Mathematics and Natural Computing” (World Scientific) and a member of the Editorial Board of The Journal of Management and Economics. Dr. Chen holds a M.A. degree in mathematics and a Ph.D. in Economics from the University of California at Los Angeles. His research interests are mainly on the applications of computational intelligence to the agent-based computational economics and finance.     

On the suitability of Biological structure determination by electron microscopy to Grid Computing

The present contribution describes a potential application of Grid Computing in Bioinformatics. High resolution structure determination of biological specimens is critical in BioSciences to understanding the biological function. The problem is computational intensive. Distributed and Grid Computing are thus becoming essential. This contribution analyzes the use of Grid Computing and its potential benefits in the field of electron microscope tomography of biological specimens. Jose-Jesus Fernandez, Ph.D.: He received his M.Sc. and Ph.D. degrees in Computer Science from the University of Granada, Spain, in 1992 and 1997, respectively. He was a Ph.D. student at the Bio-Computing unit of the National Center for BioTechnology (CNB) from the Spanish National Council of Scientific Research (CSIC), Madrid, Spain. He became an Assistant Professor in 1997 and, subsequently, Associate Professor in 2000 in Computer Architecture at the University of Almeria, Spain. He is a member of the supercomputing-algorithms research group. His research interests include high performance computing (HPC), image processing and tomography. Jose-Roman Bilbao-Castro: He received his M.Sc. degree in Computer Science from the University of Almeria in 2001. He is currently a Ph.D. student at the BioComputing unit of the CNB (CSIC) through a Ph.D. CSIC-grant in conjuction with Dept. Computer Architecture at the University of Malaga (Spain). His current research interestsinclude tomography, HPC and distributed and grid computing. Roberto Marabini, Ph.D.: He received the M.Sc. (1989) and Ph.D. (1995) degrees in Physics from the University Autonoma de Madrid (UAM) and University of Santiago de Compostela, respectively. He was a Ph.D. student at the BioComputing Unit at the CNB (CSIC). He worked at the University of Pennsylvania and the City University of New York from 1998 to 2002. At present he is an Associate Professor at the UAM. His current research interests include inverse problems, image processing and HPC. Jose-Maria Carazo, Ph.D.: He received the M.Sc. degree from the Granada University, Spain, in 1981, and got his Ph.D. in Molecular Biology at the UAM in 1984. He left for Albany, NY, in 1986, coming back to Madrid in 1989 to set up the BioComputing Unit of the CNB (CSIC). He was involved in the Spanish Ministry of Science and Technology as Deputy General Director for Research Planning. Currently, he keeps engaged in his activities at the CNB, the Scientific Park of Madrid and Integromics S.L. Immaculada Garcia, Ph.D.: She received her B.Sc. (1977) and Ph.D. (1986) degrees in Physics from the Complutense University of Madrid and University of Santiago de Compostela, respectively. From 1977 to 1987 she was an Assistant professor at the University of Granada, from 1987 to 1996 Associate professor at the University of Almeria and since 1997 she is a Full Professor and head of Dept. Computer Architecture. She is head of the supercomputing-algorithms research group. Her research interest lies in HPC for irregular problems related to image processing, global optimization and matrix computation.     

Belief revision via Lamarckian evolution

We present a system for performing belief revision in a multi-agent environment. The system is called GBR (Genetic Belief Revisor) and it is based on a genetic algorithm. In this setting, different individuals are exposed to different experiences. This may happen because the world surrounding an agent changes over time or because we allow agents exploring different parts of the world. The algorithm permits the exchange of chromosomes from different agents and combines two different evolution strategies, one based on Darwin’s and the other on Lamarck’s evolutionary theory. The algorithm therefore includes also a Lamarckian operator that changes the memes of an agent in order to improve their fitness. The operator is implemented by means of a belief revision procedure that, by tracing logical derivations, identifies the memes leading to contradiction. Moreover, the algorithm comprises a special crossover mechanism for memes in which a meme can be acquired from another agent only if the other agent has “accessed” the meme, i.e. if an application of the Lamarckian operator has read or modified the meme. Experiments have been performed on the η-queen problem and on a problem of digital circuit diagnosis. In the case of the η-queen problem, the addition of the Lamarckian operator in the single agent case improves the fitness of the best solution. In both cases the experiments show that the distribution of constraints, even if it may lead to a reduction of the fitness of the best solution, does not produce a significant reduction. Evelina Lamma, Ph.D.: She is Full Professor at the University of Ferrara. 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 extensions of logic programming languages and artificial intelligence. She was coorganizers 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. Currently, 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 1995 and his Ph.D. 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. Luís Moniz Pereira, Ph.D.: He is Full Professor of Computer Science at Departamento de Informática, Universidade Nova de Lisboa, Portugal. He received his Ph.D. in Artificial Intelligence from Brunel University in 1974. He is the director of the Artificial Intelligence Centre (CENTRIA) at Universidade Nova de Lisboa. He has been elected Fellow of the European Coordinating Committee for Artificial Intelligence in 2001. He has been a visiting Professor at the U. California at Riverside, USA, the State U. NY at Stony Brook, USA and the U. Bologna, Italy. His research interests include: knowledge representation, reasoning, learning, rational agents and logic programming.