Solving feature selection problem using intelligent double treatment iterative composite neighbourhood structure algorithm

Saif Kifah, Salwani Abdullah, Yahya Z. Arajy

Research output: Contribution to journalArticle

Abstract

Attribute reduction is one of the main contributions in rough set theory (RST) that tries to discover all possible reducts by eliminating redundant attributes while maintaining the information of the problem in hand. In this paper, we propose a meta-heuristic methodology called a double treatment iterative improvement algorithm with intelligent selection of composite neighbourhood structure, to solve the attribute reduction problems and to obtain near optimal reducts. The algorithm works iteratively by only accepting an improved solution. The proposed approach has been tried on a set of 13 benchmark datasets taken from the University of California Irvine (UCI) machine learning repository in line with the state-of-the-art methods. Thirteen datasets have been chosen due to the differences in size and complexity in order to test the stability of the proposed algorithm. The experimental results demonstrate that the proposed approach is able to produce competitive results for the tested datasets.

Original languageEnglish
Pages (from-to)255-275
Number of pages21
JournalInternational Journal of Computational Vision and Robotics
Volume7
Issue number3
DOIs
Publication statusPublished - 2017

Fingerprint

Composite structures
Feature extraction
Rough set theory
Learning systems

Keywords

  • Attribute reduction
  • CNS
  • Composite neighbourhood structure
  • INNS-CIIS 2014
  • Iterative improvement algorithm
  • Rough set theory

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

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