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. We confirm previous results stating that linear classifiers obtain higher performance than non-linear classifier using orthonormal encoding. Moreover, we prove that our new hierarchical approach is a successful attempt to obtain a drastically error reduction with respect to the performance of linear classifiers. The error rate decreases from 9.1% using linear support vector machines to 7% using the new hierarchical classifier
A. Lumini, L. Nanni (2005). Machine Learning for HIV-1 Protease Cleavage Site Prediction. INNSBRUCK : ACTA press.
Machine Learning for HIV-1 Protease Cleavage Site Prediction
LUMINI, ALESSANDRA;NANNI, LORIS
2005
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. We confirm previous results stating that linear classifiers obtain higher performance than non-linear classifier using orthonormal encoding. Moreover, we prove that our new hierarchical approach is a successful attempt to obtain a drastically error reduction with respect to the performance of linear classifiers. The error rate decreases from 9.1% using linear support vector machines to 7% using the new hierarchical classifierI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.