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Feature selection using stochastic diffusion search

Feature selection using stochastic diffusion search

Alhakbani, Haya and Al-Rifaie, Mohammad Majid ORCID: 0000-0002-1798-9615 (2017) Feature selection using stochastic diffusion search. In: GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference. ACM, pp. 385-392. ISBN 978-1450349208 (doi:https://doi.org/10.1145/3071178.3079193)

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Abstract

The method introduced in this paper uses stochastic diffusion search (SDS) to select the most relevant feature subset for the classification task. In this algorithm, SDS is adapted to find a suitable feature subset. Moreover, support vector machine (SVM) is used as a classifier to evaluate the predictive accuracy of the agent. The proposed method exhibits a statistically significant outperformance when compared with the performance of the classifier without the SDS-powered features selections. Additionally, the results have been also compared with other methods from the literature over nine datasets. It is shown that the proposed SDS based feature selection (SDS-FS) offers a competitive performance with other methods on datasets with feature size greater than 10. The behaviour of the proposed algorithm has been investigated in the context of global exploration and local exploitation.

Item Type: Conference Proceedings
Title of Proceedings: GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference
Uncontrolled Keywords: feature selection, swarm intelligence, stochastic diffusion search
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Centre for Computer & Computational Science
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 14 Jan 2021 10:11
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/30888

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