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Microarray gene expression data are often accompanied by a large number ofgenes and a small number of samples. However, only a few of these genes arerelevant to cancer, resulting in signigicant gene selection challenges. Hence,we propose a two-stage gene selection approach by combining extreme gradientboosting (XGBoost) and a multi-objective optimization genetic algorithm(XGBoost-MOGA) for cancer classification in microarray datasets. In the firststage, the genes are ranked use an ensemble-based feature selection usingXGBoost. This stage can effectively remove irrelevant genes and yield a groupcomprising the most relevant genes related to the class. In the second stage,XGBoost-MOGA searches for an optimal gene subset based on the most relevantgenes's group using a multi-objective optimization genetic algorithm. Weperformed comprehensive experiments to compare XGBoost-MOGA with otherstate-of-the-art feature selection methods using two well-known learningclassifiers on 13 publicly available microarray expression datasets. Theexperimental results show that XGBoost-MOGA yields significantly better resultsthan previous state-of-the-art algorithms in terms of various evaluationcriteria, such as accuracy, F-score, precision, and recall.

Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification