Mexican Hat Wavelet Kernel ELM for Multiclass Classification.

Jie Wang,Tian-Lei Ma,Yi-Fan Song

doi:10.1155/2017/7479140

Jie Wang, Tian-Lei Ma + Show 1 more

Open Access

https://doi.org/10.1155/2017/7479140

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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.

Highlights

Extreme learning machine, which was proposed by Huang et al [1] in 2004, is a model of single-hidden layer feedforward neural network
We propose a classifier, the Mexican Hat wavelet kernel ELM classifier, which can be applied to the multiclass classification problem
Its validity as an admissible ELM kernel is proved. This classifier solves the inevitable problems in original ELM by replacing the linear weighted mapping method with Mexican Hat wavelet

Summary

Introduction

Extreme learning machine, which was proposed by Huang et al [1] in 2004, is a model of single-hidden layer feedforward neural network. In 2006, Huang et al [5] proposed incremental extreme learning machine (I-ELM), which continuously increased the number of hidden layer nodes to improve the training accuracy. Li [6] combined I-ELM with the convex optimization learning method and proposed ECI-ELM in 2014, which reduced the training time of I-ELM This improvement overcame the weakness of randomly selecting weights in I-ELM and eventually improved the training accuracy. Akusok et al [10] proposed a high-performance ELM model in 2015, which provides a solid ground for tackling numerous Big Data challenges None of these methods has changed the characteristic of the random selection of input weights.

Preliminary Work

Mexican Hat Wavelet Kernel ELM

Performance Evaluation

Findings

Conclusion