Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/4243
Title: Ant colony optimization based feature selection in rough set theory
Authors: Mafarja, Majdi
Eleyan, Derar
Keywords: Rough sets
Artificial intelligence
Set theory
Mathematical optimization
Ants - Behavior - Mathematical models
Issue Date: 2013
Abstract: Feature selection is an important concept in rough set theory; it aims to determine a minimal subset of features that are jointly sufficient for preserving a particular property of the original data. This paper proposes an attribute reduction method that is based on Ant Colony Optimization algorithm and rough set theory as an evaluation measurement. The proposed method was tested on standard benchmark datasets. The results show that this algorithm performs well and competes other attribute reduction approaches in terms of the number of the selected features and the running time.
URI: http://hdl.handle.net/20.500.11889/4243
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