DeFs-CBDE: Clustering-guided binary mutation in multi-objective differential evolution for microarray gene selection

   DNA microarray technology produces high-dimensional gene expression data, where many genes are irrelevant to disease. Effective feature selection is thus essential to mitigate the curse of dimensionality and enhance classification performance. This study introduces a multi-objective feature selection approach employing a Clustering-Based Binary Differential Evolution (CBDE) mutation to identify a compact set of disease-relevant genes. The proposed DeFs-CBDE algorithm was assessed on four gene expression datasets, i.e. brain, breast, lung, and central nervos system cancer by selecting informative feature subsets and evaluating them using five state-of-the-art classifiers, i.e., Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors, Decision Tree (DT), and Random Forest. The DeFs-CBDE method achieved of 100 % accuracy on the brain dataset with three classifiers. On the lung dataset, DeFs-CBDE reached 97.56 % accuracy with SVM and DT. For the breast dataset, DeFs-CBDE attained 93.33 % accuracy very close to the highest score of 93.81 % accuracy. The CNS dataset proved the most challenging, where it achieved 91.67 % accuracy with SVM. Across all datasets, DeFs-CBDE consistently achieved high classification performance.

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