Integrating Rough Set Theory and Particle Swarm Optimisation in feature selection

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper proposes a new feature-selection strategy by integrating the Rough Set Theory (RST) and Particle Swarm Optimisation (PSO) algorithms to generate a set of discriminatory features for the classification problem. The proposed method is seen as a marriage between filter and wrapper approaches in which the RST is used to pre-reduce the feature set before optimisation by PSO, a meta-heuristic approach using Support Vector Machines (SVMs). Experimental results, based on the number of reducts and classification accuracy, were compared for the grid search method using data from the Machine Learning Repository. For most datasets, the proposed method statistically significantly improves the obtained classification accuracy and reduces the number of feature subsets.

Original languageEnglish
Title of host publicationProceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
Pages1009-1014
Number of pages6
DOIs
Publication statusPublished - 2010
Event2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 - Cairo
Duration: 29 Nov 20101 Dec 2010

Other

Other2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
CityCairo
Period29/11/101/12/10

Fingerprint

Rough set theory
Particle swarm optimization (PSO)
Feature extraction
Set theory
Support vector machines
Learning systems

Keywords

  • Data mining
  • Feature selection
  • Machine learning
  • Optimisation
  • Particle Swarm Optimisation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture

Cite this

Abdul-Rahman, S., Mohamed Hussein, Z. A., & Abu Bakar, A. (2010). Integrating Rough Set Theory and Particle Swarm Optimisation in feature selection. In Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 (pp. 1009-1014). [5687056] https://doi.org/10.1109/ISDA.2010.5687056

Integrating Rough Set Theory and Particle Swarm Optimisation in feature selection. / Abdul-Rahman, Shuzlina; Mohamed Hussein, Zeti Azura; Abu Bakar, Azuraliza.

Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10. 2010. p. 1009-1014 5687056.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abdul-Rahman, S, Mohamed Hussein, ZA & Abu Bakar, A 2010, Integrating Rough Set Theory and Particle Swarm Optimisation in feature selection. in Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10., 5687056, pp. 1009-1014, 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10, Cairo, 29/11/10. https://doi.org/10.1109/ISDA.2010.5687056
Abdul-Rahman S, Mohamed Hussein ZA, Abu Bakar A. Integrating Rough Set Theory and Particle Swarm Optimisation in feature selection. In Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10. 2010. p. 1009-1014. 5687056 https://doi.org/10.1109/ISDA.2010.5687056
Abdul-Rahman, Shuzlina ; Mohamed Hussein, Zeti Azura ; Abu Bakar, Azuraliza. / Integrating Rough Set Theory and Particle Swarm Optimisation in feature selection. Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10. 2010. pp. 1009-1014
@inproceedings{c37c11c92e284c5d8334b878ac496702,
title = "Integrating Rough Set Theory and Particle Swarm Optimisation in feature selection",
abstract = "This paper proposes a new feature-selection strategy by integrating the Rough Set Theory (RST) and Particle Swarm Optimisation (PSO) algorithms to generate a set of discriminatory features for the classification problem. The proposed method is seen as a marriage between filter and wrapper approaches in which the RST is used to pre-reduce the feature set before optimisation by PSO, a meta-heuristic approach using Support Vector Machines (SVMs). Experimental results, based on the number of reducts and classification accuracy, were compared for the grid search method using data from the Machine Learning Repository. For most datasets, the proposed method statistically significantly improves the obtained classification accuracy and reduces the number of feature subsets.",
keywords = "Data mining, Feature selection, Machine learning, Optimisation, Particle Swarm Optimisation",
author = "Shuzlina Abdul-Rahman and {Mohamed Hussein}, {Zeti Azura} and {Abu Bakar}, Azuraliza",
year = "2010",
doi = "10.1109/ISDA.2010.5687056",
language = "English",
isbn = "9781424481354",
pages = "1009--1014",
booktitle = "Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10",

}

TY - GEN

T1 - Integrating Rough Set Theory and Particle Swarm Optimisation in feature selection

AU - Abdul-Rahman, Shuzlina

AU - Mohamed Hussein, Zeti Azura

AU - Abu Bakar, Azuraliza

PY - 2010

Y1 - 2010

N2 - This paper proposes a new feature-selection strategy by integrating the Rough Set Theory (RST) and Particle Swarm Optimisation (PSO) algorithms to generate a set of discriminatory features for the classification problem. The proposed method is seen as a marriage between filter and wrapper approaches in which the RST is used to pre-reduce the feature set before optimisation by PSO, a meta-heuristic approach using Support Vector Machines (SVMs). Experimental results, based on the number of reducts and classification accuracy, were compared for the grid search method using data from the Machine Learning Repository. For most datasets, the proposed method statistically significantly improves the obtained classification accuracy and reduces the number of feature subsets.

AB - This paper proposes a new feature-selection strategy by integrating the Rough Set Theory (RST) and Particle Swarm Optimisation (PSO) algorithms to generate a set of discriminatory features for the classification problem. The proposed method is seen as a marriage between filter and wrapper approaches in which the RST is used to pre-reduce the feature set before optimisation by PSO, a meta-heuristic approach using Support Vector Machines (SVMs). Experimental results, based on the number of reducts and classification accuracy, were compared for the grid search method using data from the Machine Learning Repository. For most datasets, the proposed method statistically significantly improves the obtained classification accuracy and reduces the number of feature subsets.

KW - Data mining

KW - Feature selection

KW - Machine learning

KW - Optimisation

KW - Particle Swarm Optimisation

UR - http://www.scopus.com/inward/record.url?scp=79851498024&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79851498024&partnerID=8YFLogxK

U2 - 10.1109/ISDA.2010.5687056

DO - 10.1109/ISDA.2010.5687056

M3 - Conference contribution

AN - SCOPUS:79851498024

SN - 9781424481354

SP - 1009

EP - 1014

BT - Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10

ER -