Performance Evaluation of Filter-Based Gene Selection Methods in Cancer Classification

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2025

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Institute of Electrical and Electronics Engineers Inc.

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With the advances in microarray technology, gene expression levels can be measured efficiently, and this data can be used to solve important problems such as cancer classification. However, microarray data suffers from the high-dimensionality problem and requires dimensionality reduction techniques such as feature selection. This study addresses the cancer classification problem using microarray datasets and comparatively evaluates the performance of different filter-based gene (feature) selection methods. To this end, 11 microarray datasets have been evaluated using 6 different filter methods, and experimental results are presented. According to the findings, the gene selection methods used can improve classification performance by 5% to 30%. Using 5-fold cross-validation, the highest accuracy rates were achieved with 32 genes selected by the gain ratio filter for the Breast and Colon datasets, and with 8 genes selected by the information gain filter for the CNS dataset. © 2025 Elsevier B.V., All rights reserved.

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Isik University

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Cancer Classification, Dimensionality Reduction, Filter-Based Methods, Gene Selection, Microarray Data, Classification (Of Information), Dimensionality Reduction, Feature Extraction, Gene Expression, Microarrays, Cancer Classification, Filter-Based, Filter-Based Method, Gene Selection, Microarray Dataset, Microarray Technologies, Microarrays Data, Performances Evaluation, Selection Methods, Diseases

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-- 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 -- Istanbul; Isik University Sile Campus -- 211450

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