A hybrid meta-learning architecture for multi-objective optimization of SVM parameters

• Tipo: Artigo em Revista Científica Internacional
• Data: 2014

Autores

Miranda, PBC (Autor) Outra; Prudencio, RBC (Autor) Outra; de Carvalho, APLF (Autor) Outra; Carlos Soares (Autor) FEUP

Revista

Título: Neurocomputing

Vol. 143

Páginas: 27-43

ISSN: 0925-2312

Editora: Elsevier

Indexação

ISI Web of Knowledge - 25 Citações

Scopus - 32 Citações

Classificação Científica

CORDIS: Ciências Físicas > Ciência de computadores > Cibernética > Inteligência artificial

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-009-PV8

DOI: 10.1016/j.neucom.2014.06.026

Abstract (EN): Support Vector Machines (SVMs) have achieved a considerable attention due to their theoretical foundations and good empirical performance when compared to other learning algorithms in different applications. However, the SVM performance strongly depends on the adequate calibration of its parameters. In this work we proposed a hybrid multi-objective architecture which combines meta-learning (ML) with multi-objective particle swarm optimization algorithms for the SVM parameter selection problem. Given an input problem, the proposed architecture uses a ML technique to suggest an initial Pareto front of SVM configurations based on previous similar learning problems; the suggested Pareto front is then refined by a multi-objective optimization algorithm. In this combination, solutions provided by ML are possibly located in good regions in the search space. Hence, using a reduced number of successful candidates, the search process would converge faster and be less expensive. In the performed experiments, the proposed solution was compared to traditional multi-objective algorithms with random initialization, obtaining Pareto fronts with higher quality on a set of 100 classification problems.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 17