Objective cost-sensitive-boosting-WELM for handling multi class imbalance problem

Abstract

Class imbalance problem has attracted a great attention in the field of ELM (extreme learning machine). Cost sensitive ELM was proposed to address class imbalance but it merely handled binary class imbalance and required to predefine misclassification costs subjectively. Boosting WELM has been presented to handle multi class imbalance, and performed well on improving the classification accuracy of the minority class, but it may excessively strengthen minority class samples and degrade the performance of the majority class. This paper presents a method named OCS-BWELM (objective cost-sensitive-boosting-WELM) to handle multi class imbalance. It takes boosting WELM as the basic learning algorithm. The misclassification costs are determined by the distributions of the given data rather than being defined subjectively. More specifically, it seeks optimal costs through maximizing the mutual information between real targets and prediction outputs. A specific feature of OCS-BWELM is that its costs are objective. Experiments are carried out to compare our method against existing ELM related works on handling multi class imbalance. Results show that our method could achieve a better performance balance between minority class and majority class than boosting WELM. And it outperforms others in terms of G-mean, F-score and F-measures of minority classes in most cases.