Abstract
In this paper, the algorithm design of the support vector machines (SVMs) ensemble in a practical multi-stage framework is analyzed which can be implemented efficiently by evolutionary multi-objective optimization algorithm. The designing of SVMs ensemble is considered in three stages: first, the bootstrap method and a strategy of dynamical parameter range adjustment are used to generate more diverse base SVMs, and the NSGA-II algorithm which can efficiently tune the parameters of SVMs is applied to ensure the accuracy of base SVMs; Second, the NSGA-II algorithm is used again to select the member of ensemble based on the accuracy and diversity of the ensemble we have measured; Last, the reliability of different class is computed and combined to decide the outputs of ensemble in terms of the decision values of base SVMs. The proposed algorithm is applied to the UCI datasets, some useful results has been concluded for the future work in this field.
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