research-article

Ensemble mutation slime mould algorithm with restart mechanism for feature selection

Authors:

Ning CaoAuthors Info & Claims

International Journal of Intelligent Systems, Volume 37, Issue 3

Pages 2335 - 2370

https://doi.org/10.1002/int.22776

Published: 25 January 2022 Publication History

Abstract

Existing data acquisition technologies desire further improvement to meet the increasing need for big, accurate, and high‐quality data collection. Most of the collected data have redundant information such as noise. To improve the classification accuracy, the dimensionality reduction technique, which is also known as the feature selection, is a necessity for data processing. In this paper, the slime mould algorithm (SMA) is optimized by introducing the composite mutation strategy (CMS) and restart strategy (RS). The improved SMA is named CMSRSSMA, which stands for the CMS, RS, and the improved SMA. The CMS is utilized to increase the population diversity, and the RS is used to avoid the local optimum. In this paper, the CEC2017 benchmark function is used to test the effectiveness of the proposed CMSRSSMA. Then, the CMSRSSMA‐SVM model is proposed for feature selection and parameter optimization simultaneously. The performance of the model is tested by 14 data sets from UCI data repository. Experimental results show that the proposed method is superior to other algorithms in terms of classification accuracy, number of features and fitness value on most selected data sets.

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Dong JLi XZhao YJi JLi SChen H(2024)An improved binary dandelion algorithm using sine cosine operator and restart strategy for feature selectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122390239:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122390
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Published In

cover image International Journal of Intelligent Systems

International Journal of Intelligent Systems Volume 37, Issue 3

March 2022

871 pages

ISSN:0884-8173

DOI:10.1002/int.v37.3

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© 2021 Wiley Periodicals LLC.

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John Wiley and Sons Ltd.

United Kingdom

Publication History

Published: 25 January 2022

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Cited By

Dong JLi XZhao YJi JLi SChen H(2024)An improved binary dandelion algorithm using sine cosine operator and restart strategy for feature selectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122390239:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122390
Ye ALi BZhou CWang DMei EShu FShen G(2023)High-Dimensional Feature Selection Based on Improved Binary Ant Colony Optimization Combined with Hybrid Rice Optimization AlgorithmInternational Journal of Intelligent Systems10.1155/2023/14449382023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/1444938
Jia HRao HWen CMirjalili S(2023)Crayfish optimization algorithmArtificial Intelligence Review10.1007/s10462-023-10567-456:Suppl 2(1919-1979)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s10462-023-10567-4
Bao HDu YLi Y(2023)An Enhanced Fitness-Distance Balance Slime Mould Algorithm and Its Application in Feature SelectionKnowledge Science, Engineering and Management10.1007/978-3-031-40283-8_15(164-178)Online publication date: 16-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-40283-8_15
Zhong CLi GMeng Z(2022)A hybrid teaching–learning slime mould algorithm for global optimization and reliability-based design optimization problemsNeural Computing and Applications10.1007/s00521-022-07277-334:19(16617-16642)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s00521-022-07277-3

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