Support vector machines for HIV-1 protease cleavage site prediction

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 (HQ 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.