Mexican Hat Wavelet Kernel ELM for Multiclass Classification
Joint Authors
Wang, Jie
Ma, Tianlei
Song, Yi-Fan
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-21
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Jie& Song, Yi-Fan& Ma, Tianlei. 2017. Mexican Hat Wavelet Kernel ELM for Multiclass Classification. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1141103
Modern Language Association (MLA)
Wang, Jie…[et al.]. Mexican Hat Wavelet Kernel ELM for Multiclass Classification. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1141103
American Medical Association (AMA)
Wang, Jie& Song, Yi-Fan& Ma, Tianlei. Mexican Hat Wavelet Kernel ELM for Multiclass Classification. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1141103
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141103