Abstract
Ensemble learning has become a hot topic in the machine learning recently. The generalization performance of ensemble classification systems has been improved dramatically by training and combining some accurate and diverse classifiers.The model of support vector machine(SVM) ensemble based on artificial fish_swarm algorithm(AFSA) is proposed after analyzing the drawbacks of the known algorithms such as GASEN and CLU_ENN. The AFSA is used to optimize the ensemble weights of base SVMs for SVM ensemble after individual SVM of ensemble is trained. Those SVMs with weights larger than a given threshold value are ensembled. The method of selective ensemble is achieved to obtain better performance than traditional ones that ensemble all of the base SVMs. The simulated experiment results on UCI and StatLog show that the proposed method has better performance and the AFSA has its superiority on optimizing weights of SVM ensembles, and also on operation efficiency.
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Ye, Y., Gu, L., Li, S. (2012). Integrated Model of Support Vector Machine Based on Optimization of Artificial Fish Algorithm. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34041-3_55
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DOI: https://doi.org/10.1007/978-3-642-34041-3_55
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