Ensemble Feature Learning of Genomic Data Using Support Vector Machine.

ABSTRACT: The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall feature selection and classification accuracy. Surprisingly, the adoption of these methods in support vector machines has only recently received attention but mostly on classification not gene selection. This paper introduces an ensemble SVM-Recursive Feature Elimination (ESVM-RFE) for gene selection that follows the concepts of ensemble and bagging used in random forest but adopts the backward elimin

SUBMITTER: Anaissi A 

PROVIDER: S-EPMC4909287 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature