A review on the combination of binary classifiers in multiclass problems

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.     

A prototype for cartographic human body analysis

A cartographic-oriented model uses algebraic map operations to perform spatial analysis of medical data relative to the human body. A prototype system uses 3D visualization techniques to deliver analysis results. A prototype implementation suggests the model might provide the basis for a medical application tool that introduces new information insight.     

ICT integration in the classroom: Challenging the potential of a school policy

Despite the assumption that the integration of ICT influences the entire school system, research focusing on ICT in schools is generally limited to the study of variables at class level. In contrast to these studies, the present research explores ICT integration from a school improvement approach. More particularly, it examines the local school policy with respect to ICT integration from both the principal’s perspective and perceptions of teachers. Furthermore, it studies the relationship between school policies and the actual use of ICT in the classroom. To answer the research questions, a representative sample of 53 primary school principals was interviewed. In addition, the interview data were supplemented with survey data of 574 teachers from the same 53 schools. What emerged from the analyses was that school-related policies, such as an ICT plan, ICT support and ICT training have a significant effect on class use of ICT. In addition, the findings from the interviews indicate that school policies are often underdeveloped and underutilised. The discussion section focuses on challenges to improve the potential of an ICT school policy.     

Dynamic load balancing with adaptive factoring methods in scientific applications

To improve the performance of scientific applications with parallel loops, dynamic loop scheduling methods have been proposed. Such methods address performance degradations due to load imbalance caused by predictable phenomena like nonuniform data distribution or algorithmic variance, and unpredictable phenomena such as data access latency or operating system interference. In particular, methods such as factoring, weighted factoring, adaptive weighted factoring, and adaptive factoring have been developed based on a probabilistic analysis of parallel loop iterates with variable running times. These methods have been successfully implemented in a number of applications such as: N-Body and Monte Carlo simulations, computational fluid dynamics, and radar signal processing. The focus of this paper is on adaptive weighted factoring (AWF), a method that was designed for scheduling parallel loops in time-stepping scientific applications. The main contribution of the paper is to relax the time-stepping requirement, a modification that allows the AWF to be used in any application with a parallel loop. The modification further allows the AWF to adapt to load imbalance that may occur during loop execution. Results of experiments to compare the performance of the modified AWF with the performance of the other loop scheduling methods in the context of three nontrivial applications reveal that the performance of the modified method is comparable to, and in some cases, superior to the performance of the most recently introduced adaptive factoring method.

Zur personellen Verankerung der IT-Funktion im Vorstand börsennotierter Unternehmen: Ergebnisse einer inhaltsanalytischen Betrachtung

On the Rate of Board Members with IT Responsibilities in Firms Listed on the Stock Exchange: Results of a Content Analysis: This article reports on a content analysis that investigates the rate of board members with IT responsibilities in firms listed on the stock exchange in the German speaking countries. Furthermore, the study ascertains the correlation of six factors – which become evident through content analysis – with the rate of board members with IT responsibilities. A total of 679 annual reports of German, Austrian, and Swiss corporations have been analyzed. The results show that in Germany and Switzerland 26 percent of the firms investigated have a board member with IT responsibilities, in Austria the rate is 23 percent. Additionally, the results show that turnover and number of employees are highly positively correlated with the rate of board members with IT responsibilities. Furthermore, the data show a correlation between industry and the rate of board members with IT responsibilities.     

Predictive Ensemble Pruning by Expectation Propagation

An ensemble is a group of learners that work together as a committee to solve a problem. The existing ensemble learning algorithms often generate unnecessarily large ensembles, which consume extra computational resource and may degrade the generalization performance. Ensemble pruning algorithms aim to find a good subset of ensemble members to constitute a small ensemble, which saves the computational resource and performs as well as, or better than, the unpruned ensemble. This paper introduces a probabilistic ensemble pruning algorithm by choosing a set of “sparse” combination weights, most of which are zeros, to prune the ensemble. In order to obtain the set of sparse combination weights and satisfy the nonnegative constraint of the combination weights, a left-truncated, nonnegative, Gaussian prior is adopted over every combination weight. Expectation propagation (EP) algorithm is employed to approximate the posterior estimation of the weight vector. The leave-one-out (LOO) error can be obtained as a by-product in the training of EP without extra computation and is a good indication for the generalization error. Therefore, the LOO error is used together with the Bayesian evidence for model selection in this algorithm. An empirical study on several regression and classification benchmark data sets shows that our algorithm utilizes far less component learners but performs as well as, or better than, the unpruned ensemble. Our results are very competitive compared with other ensemble pruning algorithms.     

A branch and bound algorithm for extracting smallest minimal unsatisfiable subformulas

Explaining the causes of infeasibility of Boolean formulas has practical applications in numerous fields, such as artificial intelligence (repairing inconsistent knowledge bases), formal verification (abstraction refinement and unbounded model checking), and electronic design (diagnosing and correcting infeasibility). Minimal unsatisfiable subformulas (MUSes) provide useful insights into the causes of infeasibility. An unsatisfiable formula often has many MUSes. Based on the application domain, however, MUSes with specific properties might be of interest. In this paper, we tackle the problem of finding a smallest-cardinality MUS (SMUS) of a given formula. An SMUS provides a succinct explanation of infeasibility and is valuable for applications that are heavily affected by the size of the explanation. We present (1) a baseline algorithm for finding an SMUS, founded on earlier work for finding all MUSes, and (2) a new branch-and-bound algorithm called Digger that computes a strong lower bound on the size of an SMUS and splits the problem into more tractable subformulas in a recursive search tree. Using two benchmark suites, we experimentally compare Digger to the baseline algorithm and to an existing incomplete genetic algorithm approach. Digger is shown to be faster in nearly all cases. It is also able to solve far more instances within a given runtime limit than either of the other approaches.     

The Green language type system

A programming language that considers basic values and classes as objects brings more opportunities of code reuse and it is easier to use than a language that does not support this feature. However, popular statically typed object-oriented languages do not consider classes as first-class objects because this concept is difficult to integrate with static type checking. They also do not consider basic values as objects for sake of efficiency. This article presents the Green language type system which supports classes as classless objects and offers a mechanism to treat basic values as objects. The result is a reasonably simple type system which is statically typed and easy to implement. It simplifies several other language mechanisms and prevents any infinite regression of metaclasses.     

NodeWiz: Fault-tolerant grid information service

Large scale grid computing systems may provide multitudinous services, from different providers, whose quality of service will vary. Moreover, services are deployed and undeployed in the grid with no central coordination. Thus, to find out the most suitable service to fulfill their needs, or to find the most suitable set of resources on which to deploy their services, grid users must resort to a Grid Information Service (GIS). This service allows users to submit rich queries that are normally composed of multiple attributes and range operations. The ability to efficiently execute complex searches in a scalable and reliable way is a key challenge for current GIS designs. Scalability issues are normally dealt with by using peer-to-peer technologies. However, the more reliable peer-to-peer approaches do not cater for rich queries in a natural way. On the other hand, approaches that can easily support these rich queries are less robust in the presence of failures. In this paper we present the design of NodeWiz, a GIS that allows multi-attribute range queries to be performed efficiently in a distributed manner, while maintaining load balance and resilience to failures.