Hybrid ensemble learning approaches for cancer classification from gene expression data

The expression levels of genes is well-recognised to hold the keys to address many fundamental biological problems. A major application of such datasets is cancer diagnosis which is essentially a classification task. Ensemble learning, which is a powerful machine learning approach, has been widely used to improve the performance of many real-world classification problems. Ensemble learning has been also applied for cancer classification from gene expression data. This paper proposed two hybrid ensemble machine learning approaches for classifying cancer gene expression data. The first approach is the integration of random subspace ensemble with bagging, and the second one is the integration of random subspace ensemble with boosting. Experimental results show that the proposed methods can improve classification accuracy for cancer classification from gene expression data. © 2022 IEEE.