Adaptive multinomial regression with overlapping groups for multi-class classification of lung cancer

Abstract

Multi-class classification has attracted much attention in cancer diagnosis and treatment and many machine learning methods have emerged for addressing this issue recently. However, class imbalance and gene selection problems occur in classifying lung cancer data. In this paper, an adaptive multinomial regression with a sparse overlapping group lasso penalty is proposed to perform classification and grouped gene selection for lung cancer gene expression data. An overlapped grouping strategy with biological interpretability is proposed, which highlights the importance of gene groups from the minority classes. By using the conditional mutual information, the gene significance within each group is evaluated and the data-driven weights are constructed. Based on the grouping strategy and constructed weights, a regularized adaptive multinomial regression is presented and the solving algorithm is developed, which can not only select the important gene groups for each class in performing multi-class classification, but also adaptively select important genes within each group. The experiment results show that the proposed method significantly outperforms the other 6 methods on classification accuracy, and the selected genes are disease-causing genes for lung cancer.