A Novel Evolutionary Multitasking Feature Selection Approach for Genomic Data Classification

Yu Yifan, Wang Dazhi, Chen Yanhua, Wang Hongfeng, Huang Min
Proceedings of the 16th Asian Conference on Machine Learning, PMLR 260:33-48, 2025.

Abstract

Microarray-generated genomic data has recently sparked a wave of bioinformatics and data mining research. However, such data presents significant challenges for further analysis due to its high dimensionality and small sample sizes. Feature selection is a standard approach to address this issue, as it can enhance classification performance while reducing dimensionality. This paper introduces an Improved Gray Wolf Optimization-based Evolutionary Multitasking (EMT-IGWO) feature selection approach tailored for high-dimensional classification. It adopts multi-population co-evolving searching modes that can be regarded as a typical feature selection task via a specific information-sharing mechanism. Within the proposed multitasking framework, both population diversity and global searching capabilities of EMT-IGWO are improved. Moreover, several enhancements are incorporated into the two searching modes to help stagnant individuals escape from local optima with higher probabilities. Computational results show that EMT-IGWO outperforms other compared algorithms in effectiveness and efficiency evaluated across eight public gene expression datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v260-yifan25a, title = {A Novel Evolutionary Multitasking Feature Selection Approach for Genomic Data Classification}, author = {Yifan, Yu and Dazhi, Wang and Yanhua, Chen and Hongfeng, Wang and Min, Huang}, booktitle = {Proceedings of the 16th Asian Conference on Machine Learning}, pages = {33--48}, year = {2025}, editor = {Nguyen, Vu and Lin, Hsuan-Tien}, volume = {260}, series = {Proceedings of Machine Learning Research}, month = {05--08 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v260/main/assets/yifan25a/yifan25a.pdf}, url = {https://proceedings.mlr.press/v260/yifan25a.html}, abstract = {Microarray-generated genomic data has recently sparked a wave of bioinformatics and data mining research. However, such data presents significant challenges for further analysis due to its high dimensionality and small sample sizes. Feature selection is a standard approach to address this issue, as it can enhance classification performance while reducing dimensionality. This paper introduces an Improved Gray Wolf Optimization-based Evolutionary Multitasking (EMT-IGWO) feature selection approach tailored for high-dimensional classification. It adopts multi-population co-evolving searching modes that can be regarded as a typical feature selection task via a specific information-sharing mechanism. Within the proposed multitasking framework, both population diversity and global searching capabilities of EMT-IGWO are improved. Moreover, several enhancements are incorporated into the two searching modes to help stagnant individuals escape from local optima with higher probabilities. Computational results show that EMT-IGWO outperforms other compared algorithms in effectiveness and efficiency evaluated across eight public gene expression datasets.} }
Endnote
%0 Conference Paper %T A Novel Evolutionary Multitasking Feature Selection Approach for Genomic Data Classification %A Yu Yifan %A Wang Dazhi %A Chen Yanhua %A Wang Hongfeng %A Huang Min %B Proceedings of the 16th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Vu Nguyen %E Hsuan-Tien Lin %F pmlr-v260-yifan25a %I PMLR %P 33--48 %U https://proceedings.mlr.press/v260/yifan25a.html %V 260 %X Microarray-generated genomic data has recently sparked a wave of bioinformatics and data mining research. However, such data presents significant challenges for further analysis due to its high dimensionality and small sample sizes. Feature selection is a standard approach to address this issue, as it can enhance classification performance while reducing dimensionality. This paper introduces an Improved Gray Wolf Optimization-based Evolutionary Multitasking (EMT-IGWO) feature selection approach tailored for high-dimensional classification. It adopts multi-population co-evolving searching modes that can be regarded as a typical feature selection task via a specific information-sharing mechanism. Within the proposed multitasking framework, both population diversity and global searching capabilities of EMT-IGWO are improved. Moreover, several enhancements are incorporated into the two searching modes to help stagnant individuals escape from local optima with higher probabilities. Computational results show that EMT-IGWO outperforms other compared algorithms in effectiveness and efficiency evaluated across eight public gene expression datasets.
APA
Yifan, Y., Dazhi, W., Yanhua, C., Hongfeng, W. & Min, H.. (2025). A Novel Evolutionary Multitasking Feature Selection Approach for Genomic Data Classification. Proceedings of the 16th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 260:33-48 Available from https://proceedings.mlr.press/v260/yifan25a.html.

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