Hybrid Gene Selection and Classification of Cancer Microarray Data Using an Improved Binary Firefly Algorithm

Cancer microarray datasets are distinguished by their high dimensionality and a relatively small sample sizes, which presents significant challenges for accurate cancer classification. Gene selection therefore becomes essential to eliminate irrelevant genes and improve classification accuracy. This paper presents a hybrid approach combining filter and wrapper techniques for gene selection, integrating an improved binary firefly algorithm and the support vector machine classifier. The objective is to select the most cancer-related genes to decrease computation time and enhance classification model performance. Three filter methods (Information Gain Ratio, ReliefF, and Correlation-based Feature Selection) are used in ensemble with the enhanced binary firefly algorithm. The firefly algorithm's exploration and exploitation capabilities are improved through opposition-based learning during initialization and movement of the fireflies. Additionally, a mutation step is added to improve the diversity of solutions. To validate our approach, we conducted an experimental study on twelve public benchmark datasets and compared it to several recent gene selection methods used for cancer gene expression data classification. The results reveal that the suggested methodology enhances classifier performance while reducing data volume by finding a limited group of genes with strong predictive power for cancer classification.