Authors: Majdi Mafarja; Salwani Abdullah
Addresses: Data Mining and Optimisation Research Group (DMO), Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi Selangor, Malaysia; Department of Computer Science, Faculty of Information Technology, Birzeit University, P.O. Box 14, Birzeit, Palestine ' Data Mining and Optimisation Research Group (DMO), Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi Selangor, Malaysia
Abstract: Attribute reduction is the problem of selecting a minimal subset from the original set of attributes. Rough set theory has been used for attribute reduction with much success. Since it is well known that finding a minimal subset is a NP-hard problem; therefore, it is necessary to develop efficient algorithms to solve this problem. In this work, we propose a memetic algorithm-based approach inside the rough set theory which is a hybridisation of genetic algorithm and simulated annealing. The proposed method has been tested on UCI data sets. Experimental results demonstrate the effectiveness of this memetic approach when compared with previous available methods. Possible extensions upon this simple approach are also discussed.
Keywords: rough set theory; attribute reduction; memetic algorithms; genetic algorithms; simulated annealing.
International Journal of Computer Applications in Technology, 2013 Vol.48 No.3, pp.195 - 202
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 01 Oct 2013 *