Abstract
Correlation-based feature selection (CFS) using neighborhood mutual information (NMI) and particle swarm optimization (PSO) are combined into an ensemble technique in this paper. Based on this observation, an efficient gene selection algorithm, denoted by NMICFS-PSO, is proposed. Several cancer recognition tasks are gathered for testing the proposed technique. Moreover, support vector machine (SVM), integrated with leave-one-out cross-validation and served as a classifier, is employed for six classification profiles to calculate the classification accuracy. Experimental results show that the proposed method can reduce the redundant features effectively and achieve superior performance. The classification accuracy obtained by our method is higher in five out of the six gene expression problems as compared with that of other classifi cation methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.