Comparative Analysis of Filter Feature Selection Methods on Microarray Datasets

• Madhuri Gokhale

  Author

Microarray technology is an emerging technology used to analyze large-scale gene expression data simultaneously. However, interpreting gene expression data remains a challenging task because of its highdimensional and low-sample-size characteristics. Microarray datasets contain thousands of genes and only a limited number of samples, which complicates the classification process. Therefore, feature selection methods, also known as gene selection methods, are essential for identifying the most informative genes that provide maximum discriminative power between cancerous and normal tissues. Although several feature selection approaches have been proposed, there is still no universally accepted method that consistently produces optimal results across different datasets. In this study, a comparative analysis of four widely used filter-based feature selection methods, namely Chi-Square (χ2), ReliefF, Mutual Information, and Symmetrical Uncertainty, is performed on five benchmark microarray cancer datasets: Colon, Central Nervous System (CNS), Leukemia, Lung, and Ovarian datasets. The selected features are evaluated using six machine learning classifiers, including Random Forest, Decision Tree, Support Vector Machine (SVM), KNearest Neighbor (KNN), Naive Bayes, and Logistic Regression. Experimental results demonstrate that feature selection significantly improves classification performance by reducing irrelevant and redundant features. Among the evaluated classifiers, SVM combined with Mutual Information achieved the best overall performance on most datasets. The study provides a comprehensive evaluation of filterbased feature selection techniques and their impact on cancer classification accuracy using microarray data.

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