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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]

Abstract: Ant Colony Optimization (ACO) is a bioinspired metaheuristic based on ants foraging used to solve different classes of problems. In this paper, we show how, using a Two-Stage approach the quality of the solutions of ACO is improved. The Two-Stage approach can be applied to different ACO. The performance of this new approach is studied in the Traveling Salesman Problem and Quadratic Assignment Problem. The experimental results show that the obtained solutions are improved both problems using the Two-Stage approach. Several statistical procedures are applied to show the effect of this new approach. [Copyright &y& Elsevier]

Classification with imbalanced data-sets supposes a new challenge for researches in the framework of data mining. This problem appears when the number of examples that represents one of the classes of the data-set (usually the concept of interest) is much lower than that of the other classes. In this manner, the learning model must be adapted to this situation, which is very common in real applications. In this paper, we will work with fuzzy rule based classification systems using a preprocessing step in order to deal with the class imbalance. Our aim is to analyze the behaviour of fuzzy rule based classification systems in the framework of imbalanced data-sets by means of the application of an adaptive inference system with parametric conjunction operators. Our results shows empirically that the use of the this parametric conjunction operators implies a higher performance for all data-sets with different imbalanced ratios. [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]

Highlights • Study the performance of promising CNNs in the classification of coral texture images. • Analyze different types of transfer learning. • Analyze data augmentation on the performance of the coral classification model. • Experimental results outperform state-of-the-art methods needing human intervention. • Generalize the best approach to other coral texture datasets. Graphical abstract Abstract The recognition of coral species based on underwater texture images poses a significant difficulty for machine learning algorithms, due to the three following challenges embedded in the nature of this data: (1) datasets do not include information about the global structure of the coral; (2) several species of coral have very similar characteristics; and (3) defining the spatial borders between classes is difficult as many corals tend to appear together in groups. For this reasons, the classification of coral species has always required an aid from a domain expert. The objective of this paper is to develop an accurate classification model for coral texture images. Current datasets contain a large number of imbalanced classes, while the images are subject to inter-class variation. We have focused on the current small datasets and analyzed (1) several Convolutional Neural Network (CNN) architectures, (2) data augmentation techniques and (3) transfer learning approaches. We have achieved the state-of-the art accuracies using different variations of ResNet on the two small coral texture datasets, EILAT and RSMAS. [ABSTRACT FROM AUTHOR]

Security policies of information systems and networks are designed for maintaining the integrity of both the confidentiality and availability of the data for their trusted users. However, a number of malicious users analyze the vulnerabilities of these systems in order to gain unauthorized access or to compromise the quality of service. For this reason, Intrusion Detection Systems have been designed in order to monitor the system and trigger alerts whenever they found a suspicious event. Optimal Intrusion Detection Systems are those that achieve a high attack detection rate together with a small number of false alarms. However, cyber attacks present many different characteristics which make them hard to be properly identified by simple statistical methods. According to this fact, Data Mining techniques, and especially those based in Computational Intelligence, have been used for implementing robust and accuracy Intrusion Detection Systems. In this paper, we consider the use of Genetic Fuzzy Systems within a pairwise learning framework for the development of such a system. The advantages of using this approach are twofold: first, the use of fuzzy sets, and especially linguistic labels, enables a smoother borderline between the concepts, and allows a higher interpretability of the rule set. Second, the divide-and-conquer learning scheme, in which we contrast all possible pair of classes with aims, improves the precision for the rare attack events, as it obtains a better separability between a “normal activity” and the different attack types. The goodness of our methodology is supported by means of a complete experimental study, in which we contrast the quality of our results versus the state-of-the-art of Genetic Fuzzy Systems for intrusion detection and the C4.5 decision tree. [ABSTRACT FROM AUTHOR]

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]