Feature Selection for Binary Classification Within Functional Genomics Experiments via Interquartile Range and Clustering

Datasets produced in modern research, such as biomedical science, pose a number of challenges for machine learning techniques used in binary classification due to high dimensionality. Feature selection is one of the most important statistical techniques used for dimensionality reduction of the datasets. Therefore, techniques are needed to find an optimal number of features to obtain more desirable learning performance. In the machine learning context, gene selection is treated as a feature selection problem, the objective of which is to find a small subset of the most discriminative features for the target class. In this paper, a gene selection method is proposed that identifies the most discriminative genes in two stages. Genes that unambiguously assign the maximum number of samples to their respective classes using a greedy approach are selected in the first stage. The remaining genes are divided into a certain number of clusters. From each cluster, the most informative genes are selected via the lasso method and combined with genes selected in the first stage. The performance of the proposed method is assessed through comparison with other state-of-the-art feature selection methods using gene expression datasets. This is done by applying two classifiers i.e., random forest and support vector machine, on datasets with selected genes and training samples and calculating their classification accuracy, sensitivity, and Brier score on samples in the testing part. Boxplots based on the results and correlation matrices of the selected genes are thenceforth constructed. The results show that the proposed method outperforms the other methods.