Titlebook: Learning from Imbalanced Data Sets; Alberto Fernández,Salvador García,Francisco Herrer Book 2018 Springer Nature Switzerland AG 2018 Machi

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Cost-Sensitive Learning,orithms. The important issue of how to obtain the cost matrix is discussed in Sect. 4.2. Section 4.3 describes MetaCost, a popular wrapper approach for adapting any classifier to a cost-sensitive setting, while Sect. 4.4 discusses various aspects of cost-sensitive decision trees. Other cost-sensitiv

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Algorithm-Level Approaches,given to four groups of methods. First, modifications of SVMs will be discussed in Sect. 6.2. Section 6.3 will focus on skew-insensitive decision trees. Variants of NN classifiers for imbalanced problems will be presented in Sect. 6.4 and skew insensitive Bayesian in Sect. 6.5. Finally, one-class cl

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Ensemble Learning,e first introduce the foundations of ensemble learning and the most commonly considered ensemble methods for imbalanced problems, that is, Bagging and Boosting (Sect. 7.2). Then, we review the existing ensemble techniques in the framework of imbalanced datasets, focusing on two-class problems. Each

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Imbalanced Classification with Multiple Classes,mbalanced problems are enumerated in Sect. 8.4. Next, a brief experimental study to contrast some of the state-of-the-art and promising approaches in this area is carried out in Sect. 8.5. Finally, the concluding remarks are given in Sect. 8.6.

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Dimensionality Reduction for Imbalanced Learning,ill also provide the recent advances in feature selection and feature extraction by non-linear methods. In addition, we will mention a recently proposed discretization approach which is able to reduce the numeric features into categories. The chapter is organized as follows. After a short introducti

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