Towards an evolutionary algorithm: A comparison of two feature selection algorithms

Kan Chen; Huan Liu

doi:10.1109/CEC.1999.782597

Towards an evolutionary algorithm: A comparison of two feature selection algorithms

Kan Chen, Huan Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Scopus citations

Abstract

In order to deal with a large number of attributes, probabilistic feature selection algorithms have been proposed. Pure random walk entails mediocre performance in terms of search time. Introducing adaptiveness into a probabilistic algorithm can lead to a more focused search that results in a better search time. We compare two algorithms in search of an efficient but not myopic algorithm for feature selection. Based on the comparative study, we suggest some ways of improvement towards an evolutionary feature selection algorithm for data mining.

Original language	English (US)
Title of host publication	Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999
Publisher	IEEE Computer Society
Pages	1309-1313
Number of pages	5
Volume	2
DOIs	https://doi.org/10.1109/CEC.1999.782597
State	Published - 1999
Externally published	Yes
Event	1999 Congress on Evolutionary Computation, CEC 1999 - Washington, DC, United States Duration: Jul 6 1999 → Jul 9 1999

Other

Other	1999 Congress on Evolutionary Computation, CEC 1999
Country/Territory	United States
City	Washington, DC
Period	7/6/99 → 7/9/99

ASJC Scopus subject areas

Computational Mathematics

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10.1109/CEC.1999.782597

Cite this

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title = "Towards an evolutionary algorithm: A comparison of two feature selection algorithms",

abstract = "In order to deal with a large number of attributes, probabilistic feature selection algorithms have been proposed. Pure random walk entails mediocre performance in terms of search time. Introducing adaptiveness into a probabilistic algorithm can lead to a more focused search that results in a better search time. We compare two algorithms in search of an efficient but not myopic algorithm for feature selection. Based on the comparative study, we suggest some ways of improvement towards an evolutionary feature selection algorithm for data mining.",

author = "Kan Chen and Huan Liu",

year = "1999",

doi = "10.1109/CEC.1999.782597",

language = "English (US)",

volume = "2",

pages = "1309--1313",

booktitle = "Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999",

publisher = "IEEE Computer Society",

note = "1999 Congress on Evolutionary Computation, CEC 1999 ; Conference date: 06-07-1999 Through 09-07-1999",

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AU - Chen, Kan

AU - Liu, Huan

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N2 - In order to deal with a large number of attributes, probabilistic feature selection algorithms have been proposed. Pure random walk entails mediocre performance in terms of search time. Introducing adaptiveness into a probabilistic algorithm can lead to a more focused search that results in a better search time. We compare two algorithms in search of an efficient but not myopic algorithm for feature selection. Based on the comparative study, we suggest some ways of improvement towards an evolutionary feature selection algorithm for data mining.

AB - In order to deal with a large number of attributes, probabilistic feature selection algorithms have been proposed. Pure random walk entails mediocre performance in terms of search time. Introducing adaptiveness into a probabilistic algorithm can lead to a more focused search that results in a better search time. We compare two algorithms in search of an efficient but not myopic algorithm for feature selection. Based on the comparative study, we suggest some ways of improvement towards an evolutionary feature selection algorithm for data mining.

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U2 - 10.1109/CEC.1999.782597

DO - 10.1109/CEC.1999.782597

M3 - Conference contribution

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SP - 1309

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BT - Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999

PB - IEEE Computer Society

Y2 - 6 July 1999 through 9 July 1999

ER -

Towards an evolutionary algorithm: A comparison of two feature selection algorithms

Abstract

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ASJC Scopus subject areas

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