In this paper, it is proposed an optimization approach for producing reduced alphabets for peptide classification, using a Genetic Algorithm. The classification task is performed by a multi-classifier system where each classifier (Linear or Radial Basis function Support Vector Machines) is trained using features extracted by different reduced alphabets. Each alphabet is constructed by a Genetic Algorithm whose objective function is the maximization of the area under the ROC-curve obtained in several classification problems.

Lumini, A., Nanni, L. (2008). A genetic approach for building different alphabets for peptide and protein classification. BMC BIOINFORMATICS, 9, 1-10 [10.1186/1471-2105-9-45].

A genetic approach for building different alphabets for peptide and protein classification

LUMINI, ALESSANDRA;NANNI, LORIS
2008

Abstract

In this paper, it is proposed an optimization approach for producing reduced alphabets for peptide classification, using a Genetic Algorithm. The classification task is performed by a multi-classifier system where each classifier (Linear or Radial Basis function Support Vector Machines) is trained using features extracted by different reduced alphabets. Each alphabet is constructed by a Genetic Algorithm whose objective function is the maximization of the area under the ROC-curve obtained in several classification problems.
2008
Lumini, A., Nanni, L. (2008). A genetic approach for building different alphabets for peptide and protein classification. BMC BIOINFORMATICS, 9, 1-10 [10.1186/1471-2105-9-45].
Lumini, Alessandra; Nanni, Loris
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/54628
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