A dynamic model selection strategy for support vector machine classifiers

The Support Vector Machine (SVM) is a very powerful technique for general pattern recognition purposes but its efficiency in practice relies on the optimal selection of hyper-parameters. A naïve or ad hoc choice of values for these can lead to poor performance in terms of generalization error and high complexity of the parameterized models obtained in terms of the number of support vectors identified. The task of searching for optimal hyper-parameters with respect to the aforementioned performance measures is the so-called SVM model selection problem. In this paper we propose a strategy to select optimal SVM models in a dynamic fashion in order to address this problem when knowledge about the environment is updated with new observations and previously parameterized models need to be re-evaluated, and in some cases discarded in favor of revised models. This strategy combines the power of swarm intelligence theory with the conventional grid search method in order to progressively identify and sort out potential solutions using dynamically updated training datasets. Experimental results demonstrate that the proposed method outperforms the traditional approaches tested against it, while saving considerable computational time.

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► We propose a strategy to select optimal SVM models in a dynamic fashion.
► The strategy is based on the ideas of self-organization, change detection, and dynamic optimization techniques.
► The relevance of the proposed method in tracking optimal solutions and saving computational time was confirmed through experiments conducted on real and synthetic databases.