Gokalp, Osman01. Izmir Institute of Technology03. Faculty of Engineering03.04. Department of Computer Engineering2025-09-252025-09-2520259798331566555https://doi.org/10.1109/SIU66497.2025.11112199https://hdl.handle.net/11147/18457Isik UniversityWith 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.trinfo:eu-repo/semantics/closedAccessCancer ClassificationDimensionality ReductionFilter-Based MethodsGene SelectionMicroarray DataClassification (Of Information)Dimensionality ReductionFeature ExtractionGene ExpressionMicroarraysCancer ClassificationFilter-BasedFilter-Based MethodGene SelectionMicroarray DatasetMicroarray TechnologiesMicroarrays DataPerformances EvaluationSelection MethodsDiseasesConference Object2-s2.0-10501541509810.1109/SIU66497.2025.11112199