Minimum class variance class-specific extreme learning machine for imbalanced classification

Abstract

Imbalanced problems occur in real-world applications when the number of majority instances far exceeds the number of minority instances. Traditional extreme learning machine (ELM) classifier becomes biased towards the majority class due to imbalanced learning. To handle this inherent drawback, several modifications of ELM have been proposed such as weighted ELM (WELM), variances-constrained WELM (VW-ELM) to tackle the class imbalance problem effectively. One of our recent works class-specific ELM (CSELM) employs class-specific regularization and has been shown to outperform WELM for imbalanced learning. Motivated by CSELM, this work proposes a minimum class variance class-specific extreme learning machine (MCVCSELM), a variant of CSELM for tackling binary class imbalance problems more effectively. MCVCSELM uses the advantages of both the minimum class variance and the class-specific regularization. The proposed work also has lower computational complexity compared to WELM and VW-ELM. In class-specific cost regulation ELM (CCR-ELM), the calculation of the regularization parameters does not consider class distribution and class overlap. However, the performance of the CCR-ELM is comparable to ELM. MCVCSELM utilizes a class-specific regularization parameter whose value is decided by using the class proportion. The experimental results on 38 binary class datasets with different imbalanced ratios demonstrate that the proposed algorithm outperforms several state-of-the-art methods for imbalanced learning.