Abstract
Weighted combination model with appropriate weight vector is very effective in multiple classifier systems. We presented a method for determining the weight vector by particle swarm optimization in our previous work, which called PSO-WCM. A weighted combination model, PSO-LS-WCM, was proposed in this paper to improve the classification performance further, which obtained the weighted vector by particle swarm optimization with local search. We describe the algorithm of PSO-LS-WCM in detail. Seven real-world problems from UCI Machine Learning Repository were used in experiments to justify the validity of the approach. It was shown that PSO-LS-WCM is better than PSO-WCM and the other six combination methods in literature.
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Yang, L. (2011). Combining Classifiers by Particle Swarms with Local Search. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_29
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DOI: https://doi.org/10.1007/978-3-642-21524-7_29
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