Hybrid Feature Selection and Ensemble Learning Methods for Gene Selection and Cancer Classification

Abstract

A promising research field in bioinformatics and data mining is the classification of cancer based on gene expression results. Efficient sample classification is not supported by all genes. Thus, to identify the appropriate genes that help efficiently distinguish samples, a robust feature selection method is needed. Redundancy in the data on gene expression contributes to low classification performance. This paper presents the combination for gene selection and classification methods using ranking and wrapper methods. In ranking methods, information gain was used to reduce the size of dimensionality to 1% and 5%. Then, in wrapper methods K-nearest neighbors and Naïve Bayes were used with Best First, Greedy Stepwise, and Rank Search. Several combinations were investigated because it is known that no single model can give the best results using different datasets for all circumstances. Therefore, combining multiple feature selection methods and applying different classification models could provide a better decision on the final predicted cancer types. Compared with the existing classifiers, the proposed assembly gene selection methods obtained comparable performance.