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Feature selection refers to the problem of selecting relevant features whichproduce the most predictive outcome. In particular, feature selection task isinvolved in datasets containing huge number of features. Rough set theory hasbeen one of the most successful methods used for feature selection. However,this method is still not able to find optimal subsets. This paper proposes anew feature selection method based on Rough set theory hybrid with Bee ColonyOptimization (BCO) in an attempt to combat this. This proposed work is appliedin the medical domain to find the minimal reducts and experimentally comparedwith the Quick Reduct, Entropy Based Reduct, and other hybrid Rough Set methodssuch as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and ParticleSwarm Optimization (PSO).

A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization