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#5025. An ensemble filter-based heuristic approach for cancerous gene expression classification

April 2026	publication date
Proposal available till	20-05-2025
4 total number of authors per manuscript	5500 $

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Journal’s subject area:

Management Information Systems; Information Systems and Management; Software; Artificial Intelligence;

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Abstract:
Gene expression data of cancer has a huge feature set size, making its categorization a challenge for the existing classification methods. It contains redundancy, noise, and irrelevant genes. This work presents an ensemble of three filter methods, namely, Symmetrical Uncertainty (SU), chi square (X2), and Relief to reduce the feature dimensions by eliminating redundant and noisy genes. The present work designs a novel heuristic called Local Search-based Feature Selection (LSFS) that further reduces noise generated by the ensemble method. The resulting selected features are then optimized using a genetic algorithm. The obtained results are compared with five state-of-the-art algorithms based on accuracy, sensitivity, specificity, F-measure, entropy, and precision. Prediction accuracy of the proposed system on the six benchmark datasets is 99%, 90%, 98%, 94%, 98%, and 99%. Significant outcomes obtained from experimental analysis indicate that the proposed approach improves classification of cancerous gene expression data and can be used as a practical tool for the analysis of gene expression data.
Keywords:
Cancerous gene; Classification; Ensemble method; Evolutionary algorithm; Feature selection

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