Minimum variance-embedded kernelized extension of extreme learning machine for imbalance learning

Abstract

In classification problems, detecting a skew class has extensively been studied in the machine learning community. Traditional extreme learning machine (ELM) algorithm becomes biased towards the majority class due to imbalance learning. To handle this problem, several extensions of ELM have been proposed such as variances-constrained weighted ELM (VW-ELM) and class-specific kernelized ELM (CSKELM). Kernelized ELM (KELM) has a better generalization capability than traditional ELM. This work proposes novel minimum variance embedded-kernelized weighted extreme learning machine (MVKWELM) and minimum variance-embedded class-specific kernelized extreme learning machine (MVCSKELM) methods for handling the imbalanced classification problems more effectively. These methods constitute novel extensions of the VW-ELM and CSKELM classifiers respectively. This minimum variance-embedding enhances the generalization capability of the algorithm by minimizing the intra-class variance. MVCSKELM uses the advantages of both the minimum variance-embedding framework and the class-specific regularization parameters. The proposed MVCSKELM also has comparable computational complexity compared to kernelized weighted ELM (KWELM). The proposed MVCSKELM adopted class-specific regularization parameters, which are determined by using class distribution. The proposed works are evaluated using benchmark real-world imbalanced datasets downloaded from the KEEL dataset repository. The experimental results demonstrate that MVKWELM and MVCSKELM achieve superior performance in contrast to KELM, KWELM, CCR-KELM, CSKELM, RUSBoost, WKSMOTE, VW-ELM, and EasyEnsemble for imbalance learning.