Investigating memetic algorithm in solving rough set attribute reduction

Majdi Mafarja, Salwani Abdullah

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)195-202
Number of pages8
JournalInternational Journal of Computer Applications in Technology
Volume48
Issue number3
DOIs
Publication statusPublished - 2013

Fingerprint

Rough set theory
Simulated annealing
Computational complexity
Genetic algorithms

Keywords

  • Attribute reduction
  • Genetic algorithm
  • Memetic algorithm
  • Rough set theory
  • Simulated annealing

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Software
  • Information Systems
  • Computer Networks and Communications

Cite this

Investigating memetic algorithm in solving rough set attribute reduction. / Mafarja, Majdi; Abdullah, Salwani.

In: International Journal of Computer Applications in Technology, Vol. 48, No. 3, 2013, p. 195-202.

Research output: Contribution to journalArticle

@article{b32d7d67ea46480ea8abbba3faf4af43,
title = "Investigating memetic algorithm in solving rough set attribute reduction",
abstract = "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 = "Attribute reduction, Genetic algorithm, Memetic algorithm, Rough set theory, Simulated annealing",
author = "Majdi Mafarja and Salwani Abdullah",
year = "2013",
doi = "10.1504/IJCAT.2013.056915",
language = "English",
volume = "48",
pages = "195--202",
journal = "International Journal of Computer Applications in Technology",
issn = "0952-8091",
publisher = "Inderscience Enterprises Ltd",
number = "3",

}

TY - JOUR

T1 - Investigating memetic algorithm in solving rough set attribute reduction

AU - Mafarja, Majdi

AU - Abdullah, Salwani

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Attribute reduction

KW - Genetic algorithm

KW - Memetic algorithm

KW - Rough set theory

KW - Simulated annealing

UR - http://www.scopus.com/inward/record.url?scp=84885941681&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84885941681&partnerID=8YFLogxK

U2 - 10.1504/IJCAT.2013.056915

DO - 10.1504/IJCAT.2013.056915

M3 - Article

VL - 48

SP - 195

EP - 202

JO - International Journal of Computer Applications in Technology

JF - International Journal of Computer Applications in Technology

SN - 0952-8091

IS - 3

ER -