Please use this identifier to cite or link to this item:
Title: Investigating memetic algorithm in solving rough set attribute reduction
Authors: Mafarja, Majdi
Abdullah, Salwani
Keywords: Theory systems;Rough sets;Artificial intelligence;Genetic algorithms;Simulated annealing (Mathematics)
Issue Date: 2013
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
Appears in Collections:Fulltext Publications

Files in This Item:
File Description SizeFormat
Investigating Memetic Algorithm in Solving Rough Set Attribute Reduction.pdf388.55 kBAdobe PDFView/Open
Show full item record

Page view(s)

Last Week
Last month
checked on Jan 2, 2022


checked on Jan 2, 2022

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.