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Neural Computing and Applications

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01.02.2009 | Original Article

Machine learning multi-classifiers for peptide classification

verfasst von: Loris Nanni, Alessandra Lumini

Erschienen in: Neural Computing and Applications | Ausgabe 2/2009

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Abstract

In this paper, we study the performance improvement that it is possible to obtain combining classifiers based on different notions (each trained using a different physicochemical property of amino-acids). This multi-classifier has been tested in three problems: HIV-protease; recognition of T-cell epitopes; predictive vaccinology. We propose a multi-classifier that combines a classifier that approaches the problem as a two-class pattern recognition problem and a method based on a one-class classifier. Several classifiers combined with the “sum rule” enables us to obtain an improvement performance over the best results previously published in the literature.

Vorheriger Artikel Combining nearest neighbor data description and structural risk minimization for one-class classification

Nächster Artikel The behaviour of the multi-layer perceptron and the support vector regression learning methods in the prediction of NO and NO2 concentrations in Szeged, Hungary

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Titel: Machine learning multi-classifiers for peptide classification
verfasst von: Loris Nanni
Alessandra Lumini
Publikationsdatum: 01.02.2009
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 2/2009
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-007-0170-2

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