Enhancing Classification Through Multi-view Synthesis in Multi-Population Ensemble Genetic Programming

Khorshidi, MS; Yazdanjue, N; Gharoun, H; Yazdani, D; Nikoo, MR; Chen, F; Gandomi, AH

Enhancing Classification Through Multi-view Synthesis in Multi-Population Ensemble Genetic Programming

Khorshidi, MS Yazdanjue, N Gharoun, H Yazdani, D

Nikoo, MR Chen, F

Gandomi, AH

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion, 2024, pp. 2099-2102
Issue Date:: 2024-07-14

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Field	Value	Language
dc.contributor.author	Khorshidi, MS
dc.contributor.author	Yazdanjue, N
dc.contributor.author	Gharoun, H
dc.contributor.author	Yazdani, D https://orcid.org/0000-0002-7799-5013
dc.contributor.author	Nikoo, MR
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-11-20T12:54:08Z
dc.date.available	2024-11-20T12:54:08Z
dc.date.issued	2024-07-14
dc.identifier.citation	GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion, 2024, pp. 2099-2102
dc.identifier.uri	http://hdl.handle.net/10453/181992
dc.description.abstract	This study proposes a genetic programming (GP) approach for classification, integrating cooperative co-evolution with multi-view synthesis. Addressing the challenges of high-dimensional data, we enhance GP by distributing features across multiple populations, each evolving concurrently and cooperatively. Akin to multi-view ensemble learning, the segmentation of the feature set improves classifier performance by processing disparate data "views". Individuals comprise multiple genes, with a SoftMax function synthesizing gene outputs. An ensemble method combines decisions across individuals from different populations, augmenting classification accuracy and robustness. Instead of exploring the entire search space, this ensemble approach divides the search space to multiple smaller subspaces that are easier to explore and ensures that each population specializes in different aspects of the problem space. Empirical tests on multiple datasets show that the classifier obtained from proposed approach outperforms the one obtained from a single-population GP executed for the entire feature set.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation	http://purl.org/au-research/grants/arc/DE210101808
dc.relation.ispartof	GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion
dc.relation.ispartof	Proceedings of the Genetic and Evolutionary Computation Conference Companion
dc.relation.isbasedon	10.1145/3638530.3664172
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Enhancing Classification Through Multi-view Synthesis in Multi-Population Ensemble Genetic Programming
dc.type	Conference Proceeding
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-11-20T12:54:06Z
pubs.publication-status	Published

Abstract:

This study proposes a genetic programming (GP) approach for classification, integrating cooperative co-evolution with multi-view synthesis. Addressing the challenges of high-dimensional data, we enhance GP by distributing features across multiple populations, each evolving concurrently and cooperatively. Akin to multi-view ensemble learning, the segmentation of the feature set improves classifier performance by processing disparate data "views". Individuals comprise multiple genes, with a SoftMax function synthesizing gene outputs. An ensemble method combines decisions across individuals from different populations, augmenting classification accuracy and robustness. Instead of exploring the entire search space, this ensemble approach divides the search space to multiple smaller subspaces that are easier to explore and ensures that each population specializes in different aspects of the problem space. Empirical tests on multiple datasets show that the classifier obtained from proposed approach outperforms the one obtained from a single-population GP executed for the entire feature set.

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http://hdl.handle.net/10453/181992