An improved particle swarm optimization via velocity-based reinitialization for feature selection

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

1 Citation (Scopus)

Abstract

The performance of feature selection method is typically measured based on the accuracy and the number of selected features. The use of particle swarm optimization (PSO) as the feature selection method was found to be competitive than its optimization counterpart. However, the standard PSO algorithm suffers from premature convergence, a condition whereby PSO tends to get trapped in a local optimum that prevents it from being converged to a better position. This paper attempts to improve the velocity-based initialization (VBR) method on the feature selection problem using support vector machine classifier following the wrapper method strategy. Five benchmark datasets were used to implement the method. The results were analyzed based on classifier performance and the selected number of features. It was found that on average, the accuracy of the particle swarm optimization with an improved velocity-based initialization method is higher than the existing VBR method and generally generates a lesser number of features.

Original languageEnglish
Title of host publicationCommunications in Computer and Information Science
PublisherSpringer Verlag
Pages3-12
Number of pages10
Volume545
ISBN (Print)9789812879356
DOIs
Publication statusPublished - 2015
Event1st International Conference on Soft Computing in Data Science, SCDS 2015 - Putrajaya, Malaysia
Duration: 2 Sep 20153 Sep 2015

Publication series

NameCommunications in Computer and Information Science
Volume545
ISSN (Print)18650929

Other

Other1st International Conference on Soft Computing in Data Science, SCDS 2015
CountryMalaysia
CityPutrajaya
Period2/9/153/9/15

Fingerprint

Particle swarm optimization (PSO)
Feature extraction
Classifiers
Support vector machines

Keywords

  • Feature selection
  • Particle swarm optimization
  • Velocity-based reinitialization

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Abdul-Rahman, S., Abu Bakar, A., & Mohamed Hussein, Z. A. (2015). An improved particle swarm optimization via velocity-based reinitialization for feature selection. In Communications in Computer and Information Science (Vol. 545, pp. 3-12). (Communications in Computer and Information Science; Vol. 545). Springer Verlag. https://doi.org/10.1007/978-981-287-936-3_1

An improved particle swarm optimization via velocity-based reinitialization for feature selection. / Abdul-Rahman, Shuzlina; Abu Bakar, Azuraliza; Mohamed Hussein, Zeti Azura.

Communications in Computer and Information Science. Vol. 545 Springer Verlag, 2015. p. 3-12 (Communications in Computer and Information Science; Vol. 545).

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

Abdul-Rahman, S, Abu Bakar, A & Mohamed Hussein, ZA 2015, An improved particle swarm optimization via velocity-based reinitialization for feature selection. in Communications in Computer and Information Science. vol. 545, Communications in Computer and Information Science, vol. 545, Springer Verlag, pp. 3-12, 1st International Conference on Soft Computing in Data Science, SCDS 2015, Putrajaya, Malaysia, 2/9/15. https://doi.org/10.1007/978-981-287-936-3_1
Abdul-Rahman S, Abu Bakar A, Mohamed Hussein ZA. An improved particle swarm optimization via velocity-based reinitialization for feature selection. In Communications in Computer and Information Science. Vol. 545. Springer Verlag. 2015. p. 3-12. (Communications in Computer and Information Science). https://doi.org/10.1007/978-981-287-936-3_1
Abdul-Rahman, Shuzlina ; Abu Bakar, Azuraliza ; Mohamed Hussein, Zeti Azura. / An improved particle swarm optimization via velocity-based reinitialization for feature selection. Communications in Computer and Information Science. Vol. 545 Springer Verlag, 2015. pp. 3-12 (Communications in Computer and Information Science).
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