Publication Type : Conference Proceedings
Publisher : Procedia Computer Science, Elsevier B.V
Source : Procedia Computer Science, Elsevier B.V., Volume 115, p.209-217 (2017)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032446842&doi=10.1016%2fj.procs.2017.09.127&partnerID=40&md5=062332c601bbf9161333e02c148cfecd
Keywords : Comparative performance, Feature extraction, Feature selection algorithm, Feature selection methods, Fuzzy, Fuzzy filters, gene expression, Gene expression analysis, Genes, Microarray data sets, Microarray gene expression, Microarrays, Rough set theory, Roughset
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2017
Abstract : The focus of this research paper is to compare the different filter, wrapper and fuzzy rough set based feature selection methods based on three parameters namely execution time, number of features selected in the reduced subset and classifier accuracy. The results are analyzed using the different feature selection methods on cancer microarray gene expression datasets. This research work finds KNN classifier to produce higher classifier accuracy compared to traditional classifiers available in literature. Also fuzzy rough set based feature selection approach is computationally faster and produces lesser number of genes in the reduced subset compared to correlation based filter.
Cite this Research Publication : A. Chinnaswamy, Sooraj, M. P., Ramakrishnan, S., and M., G., “A Comparative Performance Evaluation of Supervised Feature Selection Algorithms on Microarray Datasets”, Procedia Computer Science, vol. 115. Elsevier B.V., pp. 209-217, 2017.