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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 7479140, 8 pages
Research Article

Mexican Hat Wavelet Kernel ELM for Multiclass Classification

School of Electrical Engineering, Zhengzhou University, Zhengzhou, China

Correspondence should be addressed to Yi-Fan Song

Received 25 November 2016; Revised 23 January 2017; Accepted 24 January 2017; Published 21 February 2017

Academic Editor: José David Martín-Guerrero

Copyright © 2017 Jie Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.