Class-specific cost regulation extreme learning machine for imbalanced classification

Due to its much faster speed and better generalization performance, extreme learning machine (ELM) has attracted much attention as an effective learning approach. However, ELM rarely involves strategies for imbalanced data distributions which may exist in many fields. Existing approaches for imbalance learning only consider the effect of the number of the class samples ignoring the dispersion degree of the data, and may lead to the suboptimal learning results. In this paper, we will propose a novel ELM, class-specific cost regulation extreme learning machine (CCR-ELM), together with its kernel based extension, for binary and multiclass classification problems with imbalanced data distributions. CCR-ELM introduces class-specific regulation cost for misclassification of each class in the performance index as the tradeoff of structural risk and empirical risk. The performance of CCR-ELM is verified using a number of benchmark datasets and the real blast furnace status diagnosis problem. Experimental results show that CCR-ELM can achieve better performance for classification problems with imbalanced data distributions than the original ELM and existing ELM imbalance learning approach, and the kernel based CCR-ELM can improve the performance further.