Particle Swarm Optimization for Feature Selection: A Review of Filter-based Classification to Identify Challenges and Opportunities

Marianne Cherrington; David Airehrour; Joan Lu; Fadi Thabtah; Qiang Xu; Samaneh Madanian

doi:10.1109/IEMCON.2019.8936185

Particle Swarm Optimization for Feature Selection: A Review of Filter-based Classification to Identify Challenges and Opportunities

Marianne Cherrington, David Airehrour, Joan Lu, Fadi Thabtah, Qiang Xu, Samaneh Madanian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Citations (Scopus)

Abstract

Feature selection (FS) is a fundamental big data task, improving classification performance by selecting a relevant feature subset to mitigate the 'curse of dimensionality'. As the number of attributes increase, search algorithms can limit FS methods. Particle swarm optimization (PSO) is a global search metaheuristic, with the ability to search a space of large dimension quickly, with few assumptions. This review explores filter FS classification methods that exploit contemporary particle swarm optimization research, categorizing state-of-the-art techniques. The major contribution of this review is in highlighting the uses and limitations of these currently underrepresented techniques, to identify current challenges and opportunities, so further productive research may be exploited.

Original language	English
Title of host publication	2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)
Editors	Satyajit Chakrabarti, Himadri Nath Saha
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	523-529
Number of pages	7
ISBN (Electronic)	9781728125305
ISBN (Print)	9781728125312
DOIs	https://doi.org/10.1109/IEMCON.2019.8936185
Publication status	Published - 19 Dec 2019
Event	10th IEEE Annual Information Technology, Electronics and Mobile Communication Conference - Vancouver, Canada Duration: 17 Oct 2019 → 19 Oct 2019 Conference number: 10 http://ieee-iemcon.org/

Conference

Conference	10th IEEE Annual Information Technology, Electronics and Mobile Communication Conference
Abbreviated title	IEMCON 2019
Country/Territory	Canada
City	Vancouver
Period	17/10/19 → 19/10/19
Internet address	http://ieee-iemcon.org/

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Cite this

Cherrington, M., Airehrour, D., Lu, J., Thabtah, F., Xu, Q., & Madanian, S. (2019). Particle Swarm Optimization for Feature Selection: A Review of Filter-based Classification to Identify Challenges and Opportunities. In S. Chakrabarti, & H. N. Saha (Eds.), 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 523-529). [8936185] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEMCON.2019.8936185

Cherrington, Marianne ; Airehrour, David ; Lu, Joan et al. / Particle Swarm Optimization for Feature Selection : A Review of Filter-based Classification to Identify Challenges and Opportunities. 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). editor / Satyajit Chakrabarti ; Himadri Nath Saha. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 523-529

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abstract = "Feature selection (FS) is a fundamental big data task, improving classification performance by selecting a relevant feature subset to mitigate the 'curse of dimensionality'. As the number of attributes increase, search algorithms can limit FS methods. Particle swarm optimization (PSO) is a global search metaheuristic, with the ability to search a space of large dimension quickly, with few assumptions. This review explores filter FS classification methods that exploit contemporary particle swarm optimization research, categorizing state-of-the-art techniques. The major contribution of this review is in highlighting the uses and limitations of these currently underrepresented techniques, to identify current challenges and opportunities, so further productive research may be exploited.",

keywords = "Classification, Feature selection (FS), Filter approaches, Machine learning (ML), Particle swarm optimization (PSO)",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Cherrington, M, Airehrour, D, Lu, J, Thabtah, F, Xu, Q & Madanian, S 2019, Particle Swarm Optimization for Feature Selection: A Review of Filter-based Classification to Identify Challenges and Opportunities. in S Chakrabarti & HN Saha (eds), 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)., 8936185, Institute of Electrical and Electronics Engineers Inc., pp. 523-529, 10th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, Vancouver, British Columbia, Canada, 17/10/19. https://doi.org/10.1109/IEMCON.2019.8936185

Particle Swarm Optimization for Feature Selection : A Review of Filter-based Classification to Identify Challenges and Opportunities. / Cherrington, Marianne; Airehrour, David; Lu, Joan et al.

2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). ed. / Satyajit Chakrabarti; Himadri Nath Saha. Institute of Electrical and Electronics Engineers Inc., 2019. p. 523-529 8936185.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Particle Swarm Optimization for Feature Selection

T2 - 10th IEEE Annual Information Technology, Electronics and Mobile Communication Conference

AU - Cherrington, Marianne

AU - Airehrour, David

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AU - Madanian, Samaneh

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AB - Feature selection (FS) is a fundamental big data task, improving classification performance by selecting a relevant feature subset to mitigate the 'curse of dimensionality'. As the number of attributes increase, search algorithms can limit FS methods. Particle swarm optimization (PSO) is a global search metaheuristic, with the ability to search a space of large dimension quickly, with few assumptions. This review explores filter FS classification methods that exploit contemporary particle swarm optimization research, categorizing state-of-the-art techniques. The major contribution of this review is in highlighting the uses and limitations of these currently underrepresented techniques, to identify current challenges and opportunities, so further productive research may be exploited.

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BT - 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

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Cherrington M, Airehrour D, Lu J, Thabtah F, Xu Q, Madanian S. Particle Swarm Optimization for Feature Selection: A Review of Filter-based Classification to Identify Challenges and Opportunities. In Chakrabarti S, Saha HN, editors, 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). Institute of Electrical and Electronics Engineers Inc. 2019. p. 523-529. 8936185 doi: 10.1109/IEMCON.2019.8936185

Particle Swarm Optimization for Feature Selection: A Review of Filter-based Classification to Identify Challenges and Opportunities

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