A validation method for comparing classifiers on imbalanced datasets, Dengesi̇z veri̇ kümeleri̇ üzeri̇nde siniflandiricilarin karşilaştirilmasi i̇çi̇n bi̇r sinama yöntemi̇

Abstract

In this study, to compare the robustness and learning capability of the classifiers on imbalanced datasets, a cross validation method that generates class-imbalanced training sets is proposed. The method will also be used to evaluate the accuracies of methods developed for dealing with the class-imbalance problem. The proposed method is used to generate imbalanced datasets from three biomedical datasets. Then, k-Nearest Neighbor, Support Vector Machines and Multi Layer Perceptron classifiers are compared using various settings of their hyper-parameters that affect their complexities. The experimental results show that SVMs are simply the most robust of all when applied to imbalanced datasets. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.