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

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

doi:10.1109/ICMLC.2012.6358949

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 Proceedings › Conference paper (refereed) › Research › peer-review

10 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 language	English
Title of host publication	Proceedings of 2012 International Conference on Machine Learning and Cybernetics
Publisher	IEEE
Pages	411-414
Number of pages	4
ISBN (Electronic)	9781467314879
ISBN (Print)	9781467314848
DOIs	https://doi.org/10.1109/ICMLC.2012.6358949
Publication status	Published - 2012
Externally published	Yes
Event	2012 International Conference on Machine Learning and Cybernetics - Sheraton Xian Hotel, Shaanxi, China Duration: 15 Jul 2012 → 17 Jul 2012

Conference

Conference	2012 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Shaanxi
Period	15/07/12 → 17/07/12

Keywords

Extreme learning machine
Minimum square error
Weighted voting

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10.1109/ICMLC.2012.6358949

Cite this

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title = "A weighted voting method using minimum square error based on extreme learning machine",

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. {\textcopyright} 2012 IEEE.",

keywords = "Extreme learning machine, Minimum square error, Weighted voting",

author = "Jing-Jing CAO and Sam KWONG and Ran WANG and Ke LI",

year = "2012",

doi = "10.1109/ICMLC.2012.6358949",

language = "English",

isbn = "9781467314848",

pages = "411--414",

booktitle = "Proceedings of 2012 International Conference on Machine Learning and Cybernetics",

publisher = "IEEE",

note = "2012 International Conference on Machine Learning and Cybernetics ; Conference date: 15-07-2012 Through 17-07-2012",

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TY - GEN

T1 - A weighted voting method using minimum square error based on extreme learning machine

AU - CAO, Jing-Jing

AU - KWONG, Sam

AU - WANG, Ran

AU - LI, Ke

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Extreme learning machine

KW - Minimum square error

KW - Weighted voting

UR - http://www.scopus.com/inward/record.url?scp=84871653301&partnerID=8YFLogxK

U2 - 10.1109/ICMLC.2012.6358949

DO - 10.1109/ICMLC.2012.6358949

M3 - Conference paper (refereed)

SN - 9781467314848

SP - 411

EP - 414

BT - Proceedings of 2012 International Conference on Machine Learning and Cybernetics

PB - IEEE

T2 - 2012 International Conference on Machine Learning and Cybernetics

Y2 - 15 July 2012 through 17 July 2012

ER -

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

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Keywords

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