Mexican Hat Wavelet Kernel ELM for Multiclass Classification

TL;DR

This paper introduces a Mexican Hat wavelet kernel for ELM, significantly enhancing multiclass classification accuracy and efficiency, validated through extensive experiments.

Contribution

It proposes a novel Mexican Hat wavelet kernel for ELM, improving multiclass classification performance and training efficiency.

Findings

Higher test accuracy on multiple datasets

Reduced training time compared to traditional KELM

Proved effectiveness of Mexican Hat wavelet as a kernel

Abstract

Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems. To some extent, it solves the existing problems of the invalid nodes and the large computational complexity in ELM. However, the traditional KELM classifier usually has a low test accuracy when it faces multiclass classification problems. In order to solve the above problem, a new classifier, Mexican Hat wavelet KELM classifier, is proposed in this paper. The proposed classifier successfully improves the training accuracy and reduces the training time in the multiclass classification problems. Moreover, the validity of the Mexican Hat wavelet as a kernel function of ELM is rigorously proved. Experimental results on different data sets show that the performance of the proposed classifier is significantly superior to the compared classifiers.