Online sequential class-specific extreme learning machine for binary imbalanced learning.

Many real-world applications suffer from the class imbalance problem, in which some classes have significantly fewer examples compared to the other classes. In this paper, we focus on online sequential learning methods, which are considerably more preferable to tackle the large size imbalanced classification problems effectively. For example, weighted online sequential extreme learning machine (WOS-ELM), voting based weighted online sequential extreme learning machine (VWOS-ELM) and weighted online sequential extreme learning machine with kernels (WOS-ELMK), etc. handle the imbalanced learning effectively. One of our recent works class-specific extreme learning machine (CS-ELM) uses class-specific regularization and has been shown to perform better for imbalanced learning. This work proposes a novel online sequential class-specific extreme learning machine (OSCSELM), which is a variant of CS-ELM. OSCSELM supports online learning technique in both chunk-by-chunk and one-by-one learning mode. It targets to handle the class imbalance problem for both small and larger datasets. The proposed work has less computational complexity in contrast with WOS-ELM for imbalanced learning. The proposed method is assessed by utilizing benchmark real-world imbalanced datasets. Experimental results illustrate the effectiveness of the proposed approach as it outperforms the other methods for imbalanced learning.