Imbalanced Extreme Learning Machine for Classification with Imbalanced Data Distributions

Abstract

Due to its much faster speed and better generalization performance, extreme learning machine (ELM) has attracted many attentions as an effective learning approach. However, ELM rarely involves strategies for imbalanced data distributions which may exist in many fields. In this paper, we will propose a novel imbalanced extreme learning machine (Im-ELM) algorithm for binary classification problems, which is applicable to the cases with both balanced and imbalanced data distributions, by addressing the classification errors for each class in the performance index, and determining the design parameters through a two-stage heuristic search method. Detailed performance comparison for Im-ELM is done based on a number of benchmark datasets for binary classification. The results show that Im-ELM can achieve better performance for classification problems with imbalanced data distributions.