Feature subset selection using genetic algorithm

Jihoon Yang; Vasant Honavar

doi:10.1109/5254.671091

Feature subset selection using genetic algorithm

Jihoon Yang, Vasant Honavar

Research output: Contribution to journal › Article › peer-review

772 Scopus citations

Abstract

Practical pattern-classification and knowledge-discovery problems require the selection of a subset of attributes or features to represent the patterns to be classified. The authors' approach uses a genetic algorithm to select such subsets, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.

Original language	English (US)
Pages (from-to)	44-48
Number of pages	5
Journal	IEEE Intelligent Systems and Their Applications
Volume	13
Issue number	2
DOIs	https://doi.org/10.1109/5254.671091
State	Published - Mar 1998

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Electrical and Electronic Engineering
Artificial Intelligence

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10.1109/5254.671091

Cite this

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title = "Feature subset selection using genetic algorithm",

abstract = "Practical pattern-classification and knowledge-discovery problems require the selection of a subset of attributes or features to represent the patterns to be classified. The authors' approach uses a genetic algorithm to select such subsets, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.",

author = "Jihoon Yang and Vasant Honavar",

note = "Funding Information: This research was partially supported by the National Science Foundation (through grants IRI-9409580 and IRI 9643299) and the John Deere Foundation.",

year = "1998",

month = mar,

doi = "10.1109/5254.671091",

language = "English (US)",

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journal = "IEEE Intelligent Systems and Their Applications",

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AU - Honavar, Vasant

N1 - Funding Information: This research was partially supported by the National Science Foundation (through grants IRI-9409580 and IRI 9643299) and the John Deere Foundation.

PY - 1998/3

Y1 - 1998/3

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AB - Practical pattern-classification and knowledge-discovery problems require the selection of a subset of attributes or features to represent the patterns to be classified. The authors' approach uses a genetic algorithm to select such subsets, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.

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Feature subset selection using genetic algorithm

Abstract

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