Dynamic tabu search for dimensionality reduction in rough set

Research output: Contribution to journalArticle

Abstract

This paper proposed a dynamic tabu search (DTSAR) that incorporated a dynamic tabu list to solve an attribute reduction problem in rough set theory. The dynamic tabu list is use to skip the aspiration criteria and to promote faster running times. A number of experiments have been conducted to evalute the performance of the proposed technique with other published metaheuristic techniques, rough sets and decision tree. DTSAR shown promising results on reduct generation time. It ranges between 0.20 minutes to 22.18 minutes. For comparison on the performance on number of reduct produced, DTSAR is on par with other metaheuristic techniques. DTSAR outperforms some techniques on certain dataset. Quality of classification rules generated by adopting DTSAR was comparable with two other methods i.e. Rough Set and Decision Trees.

Original languageEnglish
Pages (from-to)89-100
Number of pages12
JournalWSEAS Transactions on Computers
Volume11
Issue number4
Publication statusPublished - Apr 2012

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Tabu search
Decision trees
Rough set theory

Keywords

  • Attribute reduction
  • Computational intelligence
  • Dynamic Tabu list
  • Rough set
  • Tabu search

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Dynamic tabu search for dimensionality reduction in rough set. / Othman, Zalinda; Abu Bakar, Azuraliza; Abdullah, Salwani; Zakree, Mohd; Ahmad Nazri, Mohd Zakree; Sengalang, Nelly Anak.

In: WSEAS Transactions on Computers, Vol. 11, No. 4, 04.2012, p. 89-100.

Research output: Contribution to journalArticle

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