Support vector machines for HIV-1 protease cleavage site prediction

Authors:
Loris Nanni;Alessandra Lumini
Affiliations:
DEIS, IEIIT – CNR, Università di Bologna, Bologna, Italy;DEIS, IEIIT – CNR, Università di Bologna, Bologna, Italy
Venue:
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

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Abstract

Recently, several works have approached the HIV-1 protease specificity problem by applying a number of classifier creation and combination methods, from the field of machine learning. In this work we propose a hierarchical classifier (HC) architecture. Moreover, we show that radial basis function-support vector machines may obtain a lower error rate than linear support vector machines, if a step of feature selection and a step of feature transformation is performed. The error rate decreases from 9.1% using linear support vector machines to 6.85% using the new hierarchical classifier.