By Topic

Multi-objective optimization and Meta-learning for SVM parameter selection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)

Support Vector Machines (SVMs) have become a well succeed technique due to the good performance it achieves on different learning problems. However, the performance depends on adjustments on its model. The automatic SVM parameter selection is a way to deal with this. This approach is considered an optimization problem whose goal is to find suitable configuration of parameters which attends some learning problem. This work proposes the use of Particle Swarm Optimization (PSO) to treat the SVM parameter selection problem. As the design of learning systems is inherently a multi-objective optimization problem, a multi-objective PSO (MOPSO) was used to maximize the success rate and minimize the number of support vectors of the model. Moreover, we propose the combination of Meta-Learning (ML) with MOPSO to the cited problem. ML is used to recommend SVM parameters, to a given input problem, based on well-succeeded parameters adopted in previous similar problems. In this combination, initial solutions provided by ML are possibly located in good regions in the search space. Hence, using a reduced number of candidate search points, the search process, to find an adequate solution, would be less expensive. We highlight that, the combination of search algorithms with ML was just studied in the single objective field and the use of MOPSO in this context has not been investigated. In our work, we implemented a prototype in which MOPSO was used to select the values of two SVM parameters for classification problems. In the performed experiments, the proposed solution (MOPSO using ML or Hybrid MOPSO) was compared to a MOPSO with random initialization, obtaining paretos with higher quality on a set of 40 classification problems.

Published in:

The 2012 International Joint Conference on Neural Networks (IJCNN)

Date of Conference:

10-15 June 2012