SVM ensemble training for imbalanced data classification using multi-objective optimization techniques

One of the main problems with classifier training for imbalanced data is defining the correct learning criterion. On the one hand, we want the minority class to be correctly recognized, and on the other hand, we do not want to make too many mistakes in the majority class. Commonly used metrics focus either on the predictive quality of the distinguished class or propose an aggregation of simple metrics. The aggregate metrics, such as Gmean or AUC, are primarily ambiguous, i.e., they do not indicate the specific values of errors made on the minority or majority class. Additionally, improper use of aggregate metrics results in solutions selected with their help that may favor the majority class. The authors realize that a solution to this problem is using overall risk. However, this requires knowledge of the costs associated with errors made between classes, which is often unavailable. Hence, this paper will propose the semoos algorithm - an approach based on multi-objective optimization that optimizes criteria related to the prediction quality of both minority and majority classes. semoos returns a pool of non-dominated solutions from which the user can choose the model that best suits him. Automatic solution selection formulas with a so-called Pareto front have also been proposed to compare state-of-the-art methods. The proposed approach will train a svm classifier ensemble dedicated to the imbalanced data classification task. The experimental evaluations carried out on a large number of benchmark datasets confirm its usefulness.