Class-specific kernelized extreme learning machine for binary class imbalance learning

Abstract

Class imbalance problem occurs when the training dataset contains significantly fewer samples of one class (minority-class) compared to another class (majority-class). Conventional extreme learning machine (ELM) gives equal importance to all the samples leading to the results which favor the majority-class. Numerous variants of ELM-like weighted ELM (WELM), class-specific cost regulation ELM (CCR-ELM), class-specific ELM (CS-ELM) etc. have been proposed in order to diminish the performance degradation which happens due to the class imbalance problem. ELM with Gaussian kernel outperforms the ELM with Sigmoid node. This work proposed a novel class-specific kernelized ELM (CSKELM) which is a variant of kernelized ELM to address the class imbalance problem more effectively. CSKELM with Gaussian kernel function avoids the non-optimal hidden node problem associated with CS-ELM and the other existing variants of ELM. This work is distinct from WELM because it does not require the assignment of weights to the training samples. In addition, the proposed work also has considerably lower computational cost in contrast with kernelized WELM. This work employs class-specific regularization in the same way as CS-ELM. This work differs from CS-ELM as the proposed CSKELM uses the Gaussian kernel function to map the input data to the feature space. The proposed work also has lower computational overhead in contrast with kernelized CCR-ELM. The proposed work is assessed by employing benchmark real-world imbalanced datasets downloaded from the KEEL dataset repository. The experimental results indicate the superiority of the proposed work in contrast with the rest of classifiers for the imbalanced classification problems.