Multi-class Support Vector Machines Based on Arranged Decision Graphs and Particle Swarm Optimization for Model Selection

Authors:
Javier Acevedo;Saturnino Maldonado;Philip Siegmann;Sergio Lafuente;Pedro Gil
Affiliations:
University of Alcala, Teoría de la señal, Alcala de Henares, Spain;University of Alcala, Teoría de la señal, Alcala de Henares, Spain;University of Alcala, Teoría de la señal, Alcala de Henares, Spain;University of Alcala, Teoría de la señal, Alcala de Henares, Spain;University of Alcala, Teoría de la señal, Alcala de Henares, Spain
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Year:
2007

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Abstract

The use of support vector machines for multi-category problems is still an open field to research. Most of the published works use the one-against-rest strategy, but with a one-against-one approach results can be improved. To avoid testing with all the binary classifiers there are some methods like the Decision Directed Acyclic Graph based on a decision tree. In this work we propose an optimization method to improve the performance of the binary classifiers using Particle Swarm Optimization and an automatic method to build the graph that improves the average number of operations needed in the test phase. Results show a good behavior when both ideas are used.