Combining meta-learning with multi-objective particle swarm algorithms for svm parameter selection: An experimental analysis

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Miranda, PBC (Author) Other; Prudencio, RBC (Author) Other; Carvalho, ACPLF (Author) Other; Soares, C (Author) FEP

Conference proceedings International

Title: Proceedings - Brazilian Symposium on Neural Networks, SBRN

Pages: 1-6

2012 Brazilian Conference on Neural Networks, SBRN 2012

Curitiba, Parana, 20 October 2012 through 25 October 2012

Indexing

ISI Web of Knowledge

Scopus - 7 Citations

Other information

Authenticus ID: P-008-90E

DOI: 10.1109/sbrn.2012.12

Abstract (EN): Support Vector Machines (SVMs) have become a well succeeded technique due to the good performance it achieves on different learning problems. However, the SVM performance depends on adjustments of its parameters' values. The automatic SVM parameter selection is treated by many authors as an optimization problem whose goal is to find a suitable configuration of parameters for a given learning problem. This work performs a comparative study of combining Meta-Learning (ML) and Multi-Objective Particle Swarm Optimization (MOPSO) techniques for the SVM parameter selection problem. In this combination, configurations of parameters provided by ML are adopted as initial search points of the MOPSO techniques. Our hypothesis is that, starting the search with reasonable solutions will speed up the process performed by the MOPSO techniques. In our work, we implemented three MOPSO techniques applied to select two SVM parameters for classification. Our work's aim is to optimize the SVMs by seeking for configurations of parameters which maximize the success rate and minimize the number of support vectors (i.e., two objetive functions). In the experiments, the performance of the search algorithms using a traditional random initialization was compared to the performance achieved by initializing the search process using the ML suggestions. We verified that the combination of the techniques with ML obtained solutions with higher quality on a set of 40 classification problems. © 2012 IEEE.

Language: English

Type (Professor's evaluation): Scientific