Velocity-based reinitialisation approach in particle swarm optimisation for feature selection

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

2 Citations (Scopus)

Abstract

Particle Swarm Optimisation (PSO) algorithm is known to be better than Genetic Algorithm (GA) as fewer operators are needed in its algorithm. However, it still has some weaknesses such as immature convergence; a condition whereby PSO tends to get trapped in a local optimum. This condition prevents them from being converged towards a better position. Various techniques have been proposed to tackle this problem by many means. This paper attempts to integrate several velocity-based reinitialisation (VBR) approaches in PSO for solving feature selection problem. Five benchmark datasets of various features dimension were used to implement the approaches. The results were analysed based on classifier performance and the selected number of features. The findings show that the proposed VBR is generally significantly better than the existing VBR approaches.

Original languageEnglish
Title of host publicationProceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011
Pages621-624
Number of pages4
DOIs
Publication statusPublished - 2011
Event2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011 - Malacca
Duration: 5 Dec 20118 Dec 2011

Other

Other2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011
CityMalacca
Period5/12/118/12/11

Fingerprint

Particle swarm optimization (PSO)
Feature extraction
Mathematical operators
Classifiers
Genetic algorithms

Keywords

  • Feature Selection
  • Particle Swarm Optimisation
  • Velocity-based Reinitialisation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Abdul-Rahman, S., Mohamed Hussein, Z. A., & Abu Bakar, A. (2011). Velocity-based reinitialisation approach in particle swarm optimisation for feature selection. In Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011 (pp. 621-624). [6122177] https://doi.org/10.1109/HIS.2011.6122177

Velocity-based reinitialisation approach in particle swarm optimisation for feature selection. / Abdul-Rahman, Shuzlina; Mohamed Hussein, Zeti Azura; Abu Bakar, Azuraliza.

Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011. 2011. p. 621-624 6122177.

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

Abdul-Rahman, S, Mohamed Hussein, ZA & Abu Bakar, A 2011, Velocity-based reinitialisation approach in particle swarm optimisation for feature selection. in Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011., 6122177, pp. 621-624, 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011, Malacca, 5/12/11. https://doi.org/10.1109/HIS.2011.6122177
Abdul-Rahman S, Mohamed Hussein ZA, Abu Bakar A. Velocity-based reinitialisation approach in particle swarm optimisation for feature selection. In Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011. 2011. p. 621-624. 6122177 https://doi.org/10.1109/HIS.2011.6122177
Abdul-Rahman, Shuzlina ; Mohamed Hussein, Zeti Azura ; Abu Bakar, Azuraliza. / Velocity-based reinitialisation approach in particle swarm optimisation for feature selection. Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011. 2011. pp. 621-624
@inproceedings{f6e28040ee824f49a4dffe34c1fca9cd,
title = "Velocity-based reinitialisation approach in particle swarm optimisation for feature selection",
abstract = "Particle Swarm Optimisation (PSO) algorithm is known to be better than Genetic Algorithm (GA) as fewer operators are needed in its algorithm. However, it still has some weaknesses such as immature convergence; a condition whereby PSO tends to get trapped in a local optimum. This condition prevents them from being converged towards a better position. Various techniques have been proposed to tackle this problem by many means. This paper attempts to integrate several velocity-based reinitialisation (VBR) approaches in PSO for solving feature selection problem. Five benchmark datasets of various features dimension were used to implement the approaches. The results were analysed based on classifier performance and the selected number of features. The findings show that the proposed VBR is generally significantly better than the existing VBR approaches.",
keywords = "Feature Selection, Particle Swarm Optimisation, Velocity-based Reinitialisation",
author = "Shuzlina Abdul-Rahman and {Mohamed Hussein}, {Zeti Azura} and {Abu Bakar}, Azuraliza",
year = "2011",
doi = "10.1109/HIS.2011.6122177",
language = "English",
isbn = "9781457721502",
pages = "621--624",
booktitle = "Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011",

}

TY - GEN

T1 - Velocity-based reinitialisation approach in particle swarm optimisation for feature selection

AU - Abdul-Rahman, Shuzlina

AU - Mohamed Hussein, Zeti Azura

AU - Abu Bakar, Azuraliza

PY - 2011

Y1 - 2011

N2 - Particle Swarm Optimisation (PSO) algorithm is known to be better than Genetic Algorithm (GA) as fewer operators are needed in its algorithm. However, it still has some weaknesses such as immature convergence; a condition whereby PSO tends to get trapped in a local optimum. This condition prevents them from being converged towards a better position. Various techniques have been proposed to tackle this problem by many means. This paper attempts to integrate several velocity-based reinitialisation (VBR) approaches in PSO for solving feature selection problem. Five benchmark datasets of various features dimension were used to implement the approaches. The results were analysed based on classifier performance and the selected number of features. The findings show that the proposed VBR is generally significantly better than the existing VBR approaches.

AB - Particle Swarm Optimisation (PSO) algorithm is known to be better than Genetic Algorithm (GA) as fewer operators are needed in its algorithm. However, it still has some weaknesses such as immature convergence; a condition whereby PSO tends to get trapped in a local optimum. This condition prevents them from being converged towards a better position. Various techniques have been proposed to tackle this problem by many means. This paper attempts to integrate several velocity-based reinitialisation (VBR) approaches in PSO for solving feature selection problem. Five benchmark datasets of various features dimension were used to implement the approaches. The results were analysed based on classifier performance and the selected number of features. The findings show that the proposed VBR is generally significantly better than the existing VBR approaches.

KW - Feature Selection

KW - Particle Swarm Optimisation

KW - Velocity-based Reinitialisation

UR - http://www.scopus.com/inward/record.url?scp=84856722782&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84856722782&partnerID=8YFLogxK

U2 - 10.1109/HIS.2011.6122177

DO - 10.1109/HIS.2011.6122177

M3 - Conference contribution

AN - SCOPUS:84856722782

SN - 9781457721502

SP - 621

EP - 624

BT - Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011

ER -