A Novel Evolutionary Multitasking Feature Selection Approach for Genomic Data Classification

Yu Yifan, Wang Dazhi, Chen Yanhua, Wang Hongfeng, Huang Min

Keywords: Evolutionary multitasking, Gray Wolf Optimization, Feature selection, Genomic data classification

Verify Author List: I have double-checked the author list and understand that additions and removals will not be allowed after the submission deadline.

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.

Primary Area: Applications (bioinformatics, biomedical informatics, climate science, collaborative filtering, computer vision, healthcare, human activity recognition, information retrieval, natural language processing, social networks, etc.)

Paper Checklist Guidelines: I certify that all co-authors of this work have read and commit to adhering to the guidelines in Call for Papers.

Student Author: Yes

Submission Number: 20