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

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 classifier
Artificial Intelligence and Applications (AIA 2005)
406
410
A. Lumini; L. Nanni
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/6660
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