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The subgroup discovery, domain of application of CN2-SD, is defined as: “given a population of individuals and a property of those individuals, we are interested in finding a population of subgroups as large as possible and have the most unusual statistical characteristic with respect to the property of interest”. The subgroup discovery algorithm CN2-SD, based on a separate and conquer strategy, has to face the scaling problem which appears in the evaluation of large size data sets. To avoid this problem, in this paper we propose the use of instance selection algorithms for scaling down the data sets before the subgroup discovery task. The results show that CN2-SD can be executed on large data set sizes pre-processed, maintaining and improving the quality of the subgroups discovered. [Copyright &y& Elsevier]

In this paper, we focus on the experimental analysis on the performance in artificial neural networks with the use of statistical tests on the classification task. Particularly, we have studied whether the sample of results from multiple trials obtained by conventional artificial neural networks and support vector machines checks the necessary conditions for being analyzed through parametrical tests. The study is conducted by considering three possibilities on classification experiments: random variation in the selection of test data, the selection of training data and internal randomness in the learning algorithm. The results obtained state that the fulfillment of these conditions are problem-dependent and indefinite, which justifies the need of using non-parametric statistics in the experimental analysis. [Copyright &y& Elsevier]

Abstract: Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. The problem occurs, in the binary case, when the number of instances in one class significantly outnumbers the number of instances in the other class. This situation is a handicap when trying to identify the minority class, as the learning algorithms are not usually adapted to such characteristics. The approaches to deal with the problem of imbalanced datasets fall into two major categories: data sampling and algorithmic modification. Cost-sensitive learning solutions incorporating both the data and algorithm level approaches assume higher misclassification costs with samples in the minority class and seek to minimize high cost errors. Nevertheless, there is not a full exhaustive comparison between those models which can help us to determine the most appropriate one under different scenarios. The main objective of this work is to analyze the performance of data level proposals against algorithm level proposals focusing in cost-sensitive models and versus a hybrid procedure that combines those two approaches. We will show, by means of a statistical comparative analysis, that we cannot highlight an unique approach among the rest. This will lead to a discussion about the data intrinsic characteristics of the imbalanced classification problem which will help to follow new paths that can lead to the improvement of current models mainly focusing on class overlap and dataset shift in imbalanced classification. [Copyright &y& Elsevier]