A Multi-Surrogate Assisted Salp Swarm Feature Selection Algorithm with Multi-Population Adaptive Generation Strategy for Classification

Zikang Yu, Hongbin Dong, Tianyu Guo, Bingxu Zhao
Proceedings of the 15th Asian Conference on Machine Learning, PMLR 222:1590-1605, 2024.

Abstract

The salp swarm algorithm(SSA) has been successfully used to solve the feature selection problem due to its fast convergence and simple structure. However, existing SSA-based methods still suffer from the issue of low classification accuracy due to the problem of getting trapped in local optima. Therefore, this paper proposes a novel feature selection method for classification based on SSA, which can continuously generate new sub-populations to improve the search environment of the main population. Specifically, a flip-prohibition(F-P) operator is first proposed to help the main population, which may currently fall into a local optimum, find a new and more promising region. A multi-surrogate technique is suggested to evaluate the region to determine the position of sub-populations, which can reduce the high computational cost. In addition, a population initialization method is developed according to the importance of features and the dimensionality of the dataset. Finally, a communication mechanism is presented to enable different sub-populations to learn from each other. By comparing the proposed method with other 6 feature selection methods on 16 datasets, we demonstrate that the proposed method has better classification ability and can select a smaller feature subset in most cases.

Cite this Paper

BibTeX
@InProceedings{pmlr-v222-yu24a, title = {A Multi-Surrogate Assisted Salp Swarm Feature Selection Algorithm with Multi-Population Adaptive Generation Strategy for Classification}, author = {Yu, Zikang and Dong, Hongbin and Guo, Tianyu and Zhao, Bingxu}, booktitle = {Proceedings of the 15th Asian Conference on Machine Learning}, pages = {1590--1605}, year = {2024}, editor = {Yanıkoğlu, Berrin and Buntine, Wray}, volume = {222}, series = {Proceedings of Machine Learning Research}, month = {11--14 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v222/yu24a/yu24a.pdf}, url = {https://proceedings.mlr.press/v222/yu24a.html}, abstract = {The salp swarm algorithm(SSA) has been successfully used to solve the feature selection problem due to its fast convergence and simple structure. However, existing SSA-based methods still suffer from the issue of low classification accuracy due to the problem of getting trapped in local optima. Therefore, this paper proposes a novel feature selection method for classification based on SSA, which can continuously generate new sub-populations to improve the search environment of the main population. Specifically, a flip-prohibition(F-P) operator is first proposed to help the main population, which may currently fall into a local optimum, find a new and more promising region. A multi-surrogate technique is suggested to evaluate the region to determine the position of sub-populations, which can reduce the high computational cost. In addition, a population initialization method is developed according to the importance of features and the dimensionality of the dataset. Finally, a communication mechanism is presented to enable different sub-populations to learn from each other. By comparing the proposed method with other 6 feature selection methods on 16 datasets, we demonstrate that the proposed method has better classification ability and can select a smaller feature subset in most cases.} }
Endnote
%0 Conference Paper %T A Multi-Surrogate Assisted Salp Swarm Feature Selection Algorithm with Multi-Population Adaptive Generation Strategy for Classification %A Zikang Yu %A Hongbin Dong %A Tianyu Guo %A Bingxu Zhao %B Proceedings of the 15th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Berrin Yanıkoğlu %E Wray Buntine %F pmlr-v222-yu24a %I PMLR %P 1590--1605 %U https://proceedings.mlr.press/v222/yu24a.html %V 222 %X The salp swarm algorithm(SSA) has been successfully used to solve the feature selection problem due to its fast convergence and simple structure. However, existing SSA-based methods still suffer from the issue of low classification accuracy due to the problem of getting trapped in local optima. Therefore, this paper proposes a novel feature selection method for classification based on SSA, which can continuously generate new sub-populations to improve the search environment of the main population. Specifically, a flip-prohibition(F-P) operator is first proposed to help the main population, which may currently fall into a local optimum, find a new and more promising region. A multi-surrogate technique is suggested to evaluate the region to determine the position of sub-populations, which can reduce the high computational cost. In addition, a population initialization method is developed according to the importance of features and the dimensionality of the dataset. Finally, a communication mechanism is presented to enable different sub-populations to learn from each other. By comparing the proposed method with other 6 feature selection methods on 16 datasets, we demonstrate that the proposed method has better classification ability and can select a smaller feature subset in most cases.
APA
Yu, Z., Dong, H., Guo, T. & Zhao, B.. (2024). A Multi-Surrogate Assisted Salp Swarm Feature Selection Algorithm with Multi-Population Adaptive Generation Strategy for Classification. Proceedings of the 15th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 222:1590-1605 Available from https://proceedings.mlr.press/v222/yu24a.html.

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