research-article

Multi-objective particle swarm optimisation (PSO) for feature selection

Authors:

Will N. BrowneAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

Pages 81 - 88

https://doi.org/10.1145/2330163.2330175

Published: 07 July 2012 Publication History

Abstract

Feature selection (FS) is an important data preprocessing technique, which has two goals of minimising the classification error and minimising the number of features selected. Based on particle swarm optimisation (PSO), this paper proposes two multi-objective algorithms for selecting the Pareto front of non-dominated solutions (feature subsets) for classification. The first algorithm introduces the idea of non-dominated sorting based multi-objective genetic algorithm II into PSO for FS. In the second algorithm, multi-objective PSO uses the ideas of crowding, mutation and dominance to search for the Pareto front solutions. The two algorithms are compared with two single objective FS methods and a conventional FS method on nine datasets. Experimental results show that both proposed algorithms can automatically evolve a smaller number of features and achieve better classification performance than using all features and feature subsets obtained from the two single objective methods and the conventional method. Both the continuous and the binary versions of PSO are investigated in the two proposed algorithms and the results show that continuous version generally achieves better performance than the binary version. The second new algorithm outperforms the first algorithm in both continuous and binary versions.

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https://doi.org/10.1371/journal.pone.0310840
Tye YYusof UChew XTulpar S(2024)ASFE Feature Selection Technique Integrated with SMOTE-ENN for Handling High-Dimensional and Highly Imbalanced Wafer Defect Classification Data2024 17th International Conference on Advanced Computer Theory and Engineering (ICACTE)10.1109/ICACTE62428.2024.10871516(37-42)Online publication date: 13-Sep-2024
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Gao LZhou YGuo HWu Kwong S(2024)A Multiobjective Particle Swarm Optimizer based Localized Feature Selection for Imbalanced Fault Diagnosis2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10612211(1-8)Online publication date: 30-Jun-2024
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Index Terms

Multi-objective particle swarm optimisation (PSO) for feature selection
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence

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cover image ACM Conferences

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

July 2012

1396 pages

ISBN:9781450311779

DOI:10.1145/2330163

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

Copyright © 2012 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 07 July 2012

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GECCO '12

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GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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Cited By

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https://doi.org/10.1371/journal.pone.0310840
Tye YYusof UChew XTulpar S(2024)ASFE Feature Selection Technique Integrated with SMOTE-ENN for Handling High-Dimensional and Highly Imbalanced Wafer Defect Classification Data2024 17th International Conference on Advanced Computer Theory and Engineering (ICACTE)10.1109/ICACTE62428.2024.10871516(37-42)Online publication date: 13-Sep-2024
https://doi.org/10.1109/ICACTE62428.2024.10871516
Gao LZhou YGuo HWu Kwong S(2024)A Multiobjective Particle Swarm Optimizer based Localized Feature Selection for Imbalanced Fault Diagnosis2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10612211(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/CEC60901.2024.10612211
Saini RTiwari ANath ASingh PMaurya SShah M(2024)Covering assisted intuitionistic fuzzy bi-selection technique for data reduction and its applicationsScientific Reports10.1038/s41598-024-62099-814:1Online publication date: 12-Jun-2024
https://doi.org/10.1038/s41598-024-62099-8
Pham DTran BNguyen SAlahakoon DZhang M(2024)Fairness optimisation with multi-objective swarms for explainable classifiers on data streamsComplex & Intelligent Systems10.1007/s40747-024-01347-w10:4(4741-4754)Online publication date: 3-Apr-2024
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Li ESheng L(2024)Multi-Objective Feature Selection Algorithm Based on Mutual Information and NSGA-IIThe Proceedings of the 11th Frontier Academic Forum of Electrical Engineering (FAFEE2024)10.1007/978-981-97-8828-6_72(669-680)Online publication date: 30-Nov-2024
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Lu CChiang T(2024)IMF-PSO: A Particle Swarm Optimization Algorithm for Feature Selection in ClassificationTechnologies and Applications of Artificial Intelligence10.1007/978-981-97-1711-8_8(110-125)Online publication date: 28-Mar-2024
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Li NMa LXing TYu GWang CWen YCheng SGao S(2023)Automatic design of machine learning via evolutionary computation: A surveyApplied Soft Computing10.1016/j.asoc.2023.110412143(110412)Online publication date: Aug-2023
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