Int. J. of Granular Computing, Rough Sets and Intelligent Systems   »   2011 Vol.2, No.2

 

 

Title: A weighted bee colony optimisation hybrid with rough set reduct algorithm for feature selection in the medical domain

 

Authors: N. Suguna; K. Thanushkodi

 

Addresses:
Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India

 

Abstract: Feature selection refers to the problem of selecting the set of most relevant features which produces the most predictive outcome. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find the optimal subsets. This paper proposes a new feature selection method based on rough set theory hybrid with a weighted bee colony optimisation (WBCO) in an attempt to combat this. This proposed work is applied in the medical domain to find the minimal reducts and experimentally compared with the existing rough set methods, rough set methods with computational intelligence and non-rough set methods. The performance is analysed with a novel genetic algorithm-based k-nearest neighbour (GkNN) classifier. The experiments and results show that our proposed method could find optimum reducts than the other algorithms.

 

Keywords: feature selection; rough sets; quick reduct; genetic algorithms; ant colony optimisation; ACO; PSO; particle swarm optimisation; weighted BCO; bee colony optimisation; rough set theory; k-nearest neighbour classifier.

 

DOI: 10.1504/IJGCRSIS.2011.043367

 

Int. J. of Granular Computing, Rough Sets and Intelligent Systems, 2011 Vol.2, No.2, pp.123 - 140

 

Date of acceptance: 25 Apr 2011
Available online: 26 Oct 2011

 

 

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