Combining classifiers with particle swarms

Authors:
Li-ying Yang;Zheng Qin
Affiliations:
Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China;Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

Citing 7
Cited 2

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Abstract

Multiple classifier systems have shown a significant potential gain in comparison to the performance of an individual best classifier. In this paper, a weighted combination model of multiple classifier systems was presented, which took sum rule and majority vote as special cases. Particle swarm optimization (PSO), a new population-based evolutionary computation technique, was used to optimize the model. We referred the optimized model as PSO-WCM. An experimental investigation was performed on UCI data sets and encouraging results were obtained. PSO-WCM proposed in this paper is superior to other combination rules given larger data sets. It is also shown that rejection of weak classifier in the ensemble can improve classification performance further.