The analysis of high-volume microarray data faces challenges such as limited sample size, computational complexity, and the risk of inappropriate gene selection. The scarcity of samples hampers computational analysis and classification complexity, while reducing the classification's ability to generalize and predict new samples. Moreover, datasets with a high gene-to-sample ratio raise concerns about the selection of relevant genes for accurate predictive models. Interpreting disease-causing genes becomes intricate as only a subset of genes offers a precise biological insight into the disease. To address these issues, a focus on a smaller set of gene expression data is crucial for a more effective understanding of informative genes. Hence, the primary objective in microarray data analysis is to significantly reduce the number of genes through discriminative gene selection, enhancing the precision of information contained in the data. This article conducts gene expression classification on various cancer types, including colon cancer, breast cancer, leukemia, prostate tumors, and DLBCL. Each cancer type is independently evaluated in the feature selection cycle and classified using varying numbers of features. This approach aims to overcome challenges in microarray data analysis and improve the accuracy and interpretability of gene expression classification.