Framework for classification of cancer gene expression data using Bayesian hyper-parameter optimization

Computational classification of cancers is an important research problem. Gene expression data has 1000s of features, very few samples, and a class imbalance problem. In this paper, we have proposed a framework for the classification of cancer gene expression profiles. The framework consists of a pipeline of methods for data pre-processing, feature selection, and classification. Data pre-processing is done by standard scaling and normalization of the features. The feature selection is performed in two steps. First, recursive feature elimination (RFE) is used; then, a genetic algorithm is applied only in case RFE results in a feature subset of size more than a specific threshold. Next, is a meta-pool of diverse, individual as well as ensemble classifiers. Hyper-parameters of each member in the meta-pool are optimized using Bayesian Optimization. An algorithm is developed to select the best classifier from the meta-pool based on classification accuracy and computation time taken. We evaluated the framework on 6 publicly available microarray datasets and the PAN-Cancer RNA Sequencing dataset. We found that the classifier selected by the proposed framework produced significant improvement in classification accuracy and computation time required to predict labels for test datasets. A detailed comparison with the state-of-the-art methods shows that the proposed framework outperforms all of them.