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Feature Selection of Protein Structural Classification Using SVM Classifier

Paper ID Volume ID Publish Year Pages File Format Full-Text
5167 345 2013 15 PDF Available
Title
Feature Selection of Protein Structural Classification Using SVM Classifier
Abstract

Recursive feature elimination method (RFE), cross validation coefficient (CV) and accuracy of classification of test data are applied as a criterion of feature selection in order to find relevant features and to analyze their influence on classifier accuracy. Feature selection method was compared to principal component analysis (PCA) to understand the effectiveness of feature reduction. Support vector machine classifier with radial basis function (RBF) kernel is applied to find the best set of features using grid model selection and to select and assess relevant features. The best selected feature set is then analyzed and interpreted as the source of knowledge about the protein structure and biochemical properties of amino acids included in the protein domain sequence.

Keywords
pseudo amino acid composition; support vector machine; principal component analysis; recursive feature elimination; feature selection; SCOP database
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Publisher
Database: Elsevier - ScienceDirect
Journal: Biocybernetics and Biomedical Engineering - Volume 33, Issue 1, 2013, Pages 47–61
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us