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

7 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 - International Conference on Machine Learning and Cybernetics
Pages	411-414
DOIs	https://doi.org/10.1109/ICMLC.2012.6358949
Publication status	Published - 2012
Externally published	Yes

Keywords

Extreme learning machine
Minimum square error
Weighted voting

Access to Document

10.1109/ICMLC.2012.6358949

Cite this

@inproceedings{e25a4c7b346746758218db25be620612,

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",

pages = "411--414",

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

}

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)

SP - 411

EP - 414

BT - Proceedings - International Conference on Machine Learning and Cybernetics

ER -

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

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this