A weighted voting method using minimum square error based on extreme learning machine

Jing-Jing CAO, Sam KWONG, Ran WANG, Ke LI

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

9 Citations (Scopus)

Abstract

Extreme Learning Machine (ELM) has become popular for solving classification problem due to its fast speed. However, the system of ELM may be unreliable since its performance often relies on random input hidden node parameters. The techniques of combining multiple classifiers are widely adopted to improve both reliability and accuracy of a single classifier. Thus, this paper presents a minimum square error (MSE) based weighted voting method to optimize the linear combination of multiple ELMs. The experimental results over ten VCI data sets show better classification performance than the original ELM and the voting based ELM classifiers. © 2012 IEEE.
Original languageEnglish
Title of host publicationProceedings of 2012 International Conference on Machine Learning and Cybernetics
PublisherIEEE
Pages411-414
Number of pages4
ISBN (Electronic)9781467314879
ISBN (Print)9781467314848
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 International Conference on Machine Learning and Cybernetics - Sheraton Xian Hotel, Shaanxi, China
Duration: 15 Jul 201217 Jul 2012

Conference

Conference2012 International Conference on Machine Learning and Cybernetics
Country/TerritoryChina
CityShaanxi
Period15/07/1217/07/12

Keywords

  • Extreme learning machine
  • Minimum square error
  • Weighted voting

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