Investigating composite neighbourhood structure for attribute reduction in rough set theory

Saif Kifah Jihad, Salwani Abdullah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

Attribute reduction is one of the main issues in the theoretical research of rough set theory which is known as a NP-hard optimization problem. The objective is to find the minimal number of attributes from a large dataset. Hence it is difficult to solve to optimality. This paper proposes a composite neighbourhood structure approach to solve the attribute reduction problem that consists of two versions. The first version is a basic composite neighbourhood structure (CNS) approach where the neighbourhood is selected at random. For the second version, the selection of the neighbourhood structure is based on certain rules (coded as IS-CNS). Both of the algorithms only accept an improved solution. The proposed approach is tested on a set of benchmark datasets taken from University of California, Irvine (UCI) machine learning respiratory in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed approach is able to produce competitive results for the test datasets.

Original languageEnglish
Title of host publicationProceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
Pages1183-1188
Number of pages6
DOIs
Publication statusPublished - 2010
Event2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 - Cairo
Duration: 29 Nov 20101 Dec 2010

Other

Other2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
CityCairo
Period29/11/101/12/10

Fingerprint

Rough set theory
Composite structures
Learning systems

Keywords

  • Attribute reduction
  • Composite neighbourhood structure

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture

Cite this

Jihad, S. K., & Abdullah, S. (2010). Investigating composite neighbourhood structure for attribute reduction in rough set theory. In Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 (pp. 1183-1188). [5687026] https://doi.org/10.1109/ISDA.2010.5687026

Investigating composite neighbourhood structure for attribute reduction in rough set theory. / Jihad, Saif Kifah; Abdullah, Salwani.

Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10. 2010. p. 1183-1188 5687026.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jihad, SK & Abdullah, S 2010, Investigating composite neighbourhood structure for attribute reduction in rough set theory. in Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10., 5687026, pp. 1183-1188, 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10, Cairo, 29/11/10. https://doi.org/10.1109/ISDA.2010.5687026
Jihad SK, Abdullah S. Investigating composite neighbourhood structure for attribute reduction in rough set theory. In Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10. 2010. p. 1183-1188. 5687026 https://doi.org/10.1109/ISDA.2010.5687026
Jihad, Saif Kifah ; Abdullah, Salwani. / Investigating composite neighbourhood structure for attribute reduction in rough set theory. Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10. 2010. pp. 1183-1188
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