Bat algorithm for rough set attribute reduction

Ahmed Majid Taha, Yee Chong Tang

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

Attribute reduction (AR) refers to the problem of choosing an optimal subset of attributes from a larger set of possible attributes that are most predictive for a given result. AR techniques have recently attracted attention due to its importance in many areas such as pattern recognition, machine learning and signal processing. In this paper, a new optimization method has been introduced called bat algorithm for attribute reduction (BAAR), the proposed method is based mainly on the echolocation behavior of bats. BAAR is verified using 13 benchmark datasets. Experimental results show that the performances of the proposed method when compared to other features selection methods achieve equal or better performance.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalJournal of Theoretical and Applied Information Technology
Volume51
Issue number1
Publication statusPublished - 01 Jan 2013

Fingerprint

Attribute Reduction
Rough Set
Attribute
Large Set
Feature Selection
Pattern Recognition
Pattern recognition
Optimization Methods
Learning systems
Signal Processing
Feature extraction
Machine Learning
Signal processing
Benchmark
Subset
Experimental Results

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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Bat algorithm for rough set attribute reduction. / Taha, Ahmed Majid; Tang, Yee Chong.

In: Journal of Theoretical and Applied Information Technology, Vol. 51, No. 1, 01.01.2013, p. 1-8.

Research output: Contribution to journalArticle

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