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.
This work is supported by the Major State Basic Research Development Program of China (973 Program), No.2004CB719401.
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Yang, Ly., Qin, Z. (2005). Combining Classifiers with Particle Swarms. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_106
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DOI: https://doi.org/10.1007/11539117_106
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