Sparsity-driven weighted ensemble classifier
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In this letter, a novel weighted ensemble classifier is proposed that improves classification accuracy and minimizes the number of classifiers. Ensemble weight finding problem is modeled as a cost function with following terms: (a) a data fidelity term aiming to decrease misclassification rate, (b) a sparsity term aiming to decrease the number of classifiers, and (c) a non-negativity constraint on the weights of the classifiers. The proposed cost function is a non-convex and hard to solve; thus, convex relaxation techniques and novel approximations are employed to obtain a numerically efficient solution. The proposed method achieves better or similar performance compared to state-of-the art classifier ensemble methods, while using lower number of classifiers.
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