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

• Type: Article in International Scientific Journal
• Date: 2014

Authors

Miranda, PBC (Author) Other; Prudencio, RBC (Author) Other; de Carvalho, APLF (Author) Other; Carlos Soares (Author) FEUP

Journal

Title: Neurocomputing

Vol. 143

Pages: 27-43

ISSN: 0925-2312

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 25 Citations

Scopus - 32 Citations

Scientific classification

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17