Feature extraction using rough set theory and genetic algorithms - An application for the simplification of product quality evaluation

Lian Yin Zhai, Li Pheng Khoo, Sai Cheong Fok

Research output: Contribution to journalArticlepeer-review

109 Citations (Scopus)

Abstract

Feature extraction is an important aspect in data mining and knowledge discovery. In this paper an integrated feature extraction approach, which is based on rough set theory and genetic algorithms (GAs), is proposed. Based on this approach, a prototype feature extraction system has been established and illustrated in an application for the simplification of product quality evaluation. The prototype system successfully integrates the capability of rough set theory in handling uncertainty with a robust search engine, which is based on a GA. The results show that it can remarkably reduce the cost and time consumed on product quality evaluation without compromising the overall specifications of the acceptance tests.

Original languageEnglish
Pages (from-to)661-676
Number of pages16
JournalComputers and Industrial Engineering
Volume43
Issue number4
DOIs
Publication statusPublished - Sep 2002
Externally publishedYes

Keywords

  • Feature extraction
  • Genetic algorithm
  • Knowledge extraction
  • Rough set

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Information Systems and Management
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

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