Title: A novel feature selection based on apriori property and correlation analysis for protein sequence classification using MapReduce
Authors: R. Bhavani; G. Sudha Sadasivam
Addresses: Department of Computer Science and Engineering, Government College of Technology, Coimbatore, Tamil Nadu, India ' Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Abstract: Feature selection is a crucial step in classification of protein sequences into existing superfamilies. Classifying protein sequences into different families based on their sequence patterns is helpful in predicting the structure and function of protein. This paper proposes a novel feature selection algorithm which first transforms the protein sequences into feature vectors and reduces the size of the feature vector based on the apriori property and correlation measure using MapReduce programming on Hadoop framework. Experimental results show that the proposed method of feature selection reduces the features by 99% and also improves accuracy by 5% to 6%.
Keywords: apriori property; sequence classification; correlation analysis; feature subset selection; MapReduce; bioinformatics.
International Journal of Data Mining and Bioinformatics, 2017 Vol.17 No.3, pp.255 - 265
Available online: 17 Jul 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article