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Performance Analysis of Classifiers on Filter-Based Feature Selection Approaches on Microarray Data

Performance Analysis of Classifiers on Filter-Based Feature Selection Approaches on Microarray Data

Arunkumar Chinnaswamy, Ramakrishnan Srinivasan
Copyright: © 2017 |Pages: 30
ISBN13: 9781522523758|ISBN10: 1522523758|EISBN13: 9781522523765
DOI: 10.4018/978-1-5225-2375-8.ch002
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MLA

Chinnaswamy, Arunkumar, and Ramakrishnan Srinivasan. "Performance Analysis of Classifiers on Filter-Based Feature Selection Approaches on Microarray Data." Bio-Inspired Computing for Information Retrieval Applications, edited by D.P. Acharjya and Anirban Mitra, IGI Global, 2017, pp. 41-70. https://doi.org/10.4018/978-1-5225-2375-8.ch002

APA

Chinnaswamy, A. & Srinivasan, R. (2017). Performance Analysis of Classifiers on Filter-Based Feature Selection Approaches on Microarray Data. In D. Acharjya & A. Mitra (Eds.), Bio-Inspired Computing for Information Retrieval Applications (pp. 41-70). IGI Global. https://doi.org/10.4018/978-1-5225-2375-8.ch002

Chicago

Chinnaswamy, Arunkumar, and Ramakrishnan Srinivasan. "Performance Analysis of Classifiers on Filter-Based Feature Selection Approaches on Microarray Data." In Bio-Inspired Computing for Information Retrieval Applications, edited by D.P. Acharjya and Anirban Mitra, 41-70. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2375-8.ch002

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Abstract

The process of Feature selection in machine learning involves the reduction in the number of features (genes) and similar activities that results in an acceptable level of classification accuracy. This paper discusses the filter based feature selection methods such as Information Gain and Correlation coefficient. After the process of feature selection is performed, the selected genes are subjected to five classification problems such as Naïve Bayes, Bagging, Random Forest, J48 and Decision Stump. The same experiment is performed on the raw data as well. Experimental results show that the filter based approaches reduce the number of gene expression levels effectively and thereby has a reduced feature subset that produces higher classification accuracy compared to the same experiment performed on the raw data. Also Correlation Based Feature Selection uses very fewer genes and produces higher accuracy compared to Information Gain based Feature Selection approach.

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