  Microarray gene expression data are often accompanied by a large number of
genes and a small number of samples. However, only a few of these genes are
relevant to cancer, resulting in signigicant gene selection challenges. Hence,
we propose a two-stage gene selection approach by combining extreme gradient
boosting (XGBoost) and a multi-objective optimization genetic algorithm
(XGBoost-MOGA) for cancer classification in microarray datasets. In the first
stage, the genes are ranked use an ensemble-based feature selection using
XGBoost. This stage can effectively remove irrelevant genes and yield a group
comprising the most relevant genes related to the class. In the second stage,
XGBoost-MOGA searches for an optimal gene subset based on the most relevant
genes's group using a multi-objective optimization genetic algorithm. We
performed comprehensive experiments to compare XGBoost-MOGA with other
state-of-the-art feature selection methods using two well-known learning
classifiers on 13 publicly available microarray expression datasets. The
experimental results show that XGBoost-MOGA yields significantly better results
than previous state-of-the-art algorithms in terms of various evaluation
criteria, such as accuracy, F-score, precision, and recall.

Machine Learning