A hybrid feature selection method for biomarker identification

Min Wang; Minqing Zhao; Weidong Xie

doi:10.1117/12.3049784

8 November 2024 A hybrid feature selection method for biomarker identification

Min Wang, Minqing Zhao, Weidong Xie

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134163J (2024) https://doi.org/10.1117/12.3049784
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Identifying disease-specific biomarkers from gene microarray data is crucial for disease diagnosis. Genetic algorithms can effectively select important features through efficient search strategies. However, traditional genetic algorithms face challenges such as retaining too many features, slow convergence, and instability due to the high dimensionality and redundancy of the data, as well as multiple random factors in the algorithm. This paper proposes a hybrid algorithm combining Maximum Relevance Minimum Redundancy and a Population Clustering Binary Genetic Algorithm. The proposed method selects the most important biomarkers through an initialization strategy based on Opposition-Based Learning, clustering, and dynamic mutation. Experimental results demonstrate that the proposed method achieves higher classification accuracy with fewer features across six high-dimensional microarray datasets.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Min Wang, Minqing Zhao, and Weidong Xie "A hybrid feature selection method for biomarker identification", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134163J (8 November 2024); https://doi.org/10.1117/12.3049784

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