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An improved protein fold recognition with support vector machines
Author(s) -
Chmielnicki Wiesław,
RotermanKonieczna Irena,
Stąpor Katarzyna
Publication year - 2012
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2010.00572.x
Subject(s) - support vector machine , computer science , classifier (uml) , artificial intelligence , pattern recognition (psychology) , machine learning , binary classification , margin classifier , fold (higher order function) , binary number , structured support vector machine , mathematics , arithmetic , programming language
Abstract Predicting the three‐dimensional structure (fold) of a protein is a key problem in molecular biology. It is also interesting issue for statistical methods recognition. In this paper a multi‐class support vector machine (SVM) classifier is used on a real world data set. The SVM is a binary classifier, but protein fold recognition is a multi‐class problem. So several new approaches to deal with this issue are presented including a modification of the well‐known one‐versus‐one strategy. However, in this strategy the number of different binary classifiers that must be trained is quickly increasing with the number of classes. The methods proposed in this paper show how this problem can be overcome.