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 languageEnglish (US)
Pages (from-to)44-48
Number of pages5
JournalIEEE Intelligent Systems and Their Applications
Volume13
Issue number2
DOIs
StatePublished - Mar 1 1998

Fingerprint

Set theory
Pattern recognition
Data mining
Genetic algorithms
Costs

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence

Cite this

@article{bd78748d20df40cbb9b738338041d0f6,
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",
year = "1998",
month = "3",
day = "1",
doi = "10.1109/5254.671091",
language = "English (US)",
volume = "13",
pages = "44--48",
journal = "IEEE Intelligent Systems",
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number = "2",

}

Feature subset selection using genetic algorithm. / Yang, Jihoon; Honavar, Vasant.

In: IEEE Intelligent Systems and Their Applications, Vol. 13, No. 2, 01.03.1998, p. 44-48.

Research output: Contribution to journalArticle

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