Metaheuristic approach for an enhanced mRMR filter method for classification using drug response microarray data

Quality data mining analysis based on microarray gene expression data is a good approach for disease classification and other fields, such as pharmacology, as well as a useful tool for medical innovation. One of the challenges in classification is that microarrays involve high dimensionality and a large number of redundant and irrelevant features. Feature selection is the most popular method for determining the optimal number of features that will be used for classification. Feature selection is important to accelerate learning, which is represented only by the optimal feature subset. The current approach for microarray feature selection for the filter method is to simply select the top-ranked genes, i.e., keeping the 50 or 100 best-ranked genes. However, the current approach is determined by human intuition; it requires trial and error, and thus, is time-consuming. Accordingly, this study aims to propose a metaheuristic approach for selecting the top n relevant genes in drug microarray data to enhance the minimum redundancy-maximum relevance (mRMR) filter method. Three metaheuristics are applied, namely, particle swarm optimization (PSO), cuckoo search (CS), and artificial bee colony (ABC). Subsequently, k-nearest neighbor and support vector machine are used as classifiers to evaluate classification performance. The experiment used a microarray gene dataset of liver xenobiotic and pharmacological responses. Experimental results show that meta-heuristic is more efficient approaches that have reduced the complexity of the classifier. Furthermore, the results show that mRMR-CS exhibits the best performance compared with mRMR-PSO and mRMR-ABC.