Mexican Hat Wavelet Kernel ELM for Multiclass Classification

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.

An Online Calibration Method for a Galvanometric System Based on Wavelet Kernel ELM

“…However, the ELM algorithm uses linear weighted mapping to train the calibration data sets. For nonlinear samples, the accuracy of ELM is reduced [28]. In addition, the ELM algorithm randomly selects the weights and uses the trial and error method to determine the optimal number of neurons, which will affect the stability and rigor of the calibration algorithm.…”

The effects of age and body weight on powerlifters: An analysis model of powerlifting performance based on machine learning

“…In addition, KELM has the advantages of fast convergence and good generalization. In recent years, the algorithm has been applied widely (Wang et al, 2019;Wang, Song, & Ma, 2017;Zhao et al, 2017).…”