Sparsity-driven weighted ensemble classifier

TL;DR

This paper introduces a sparsity-driven weighted ensemble classifier (SDWEC) that enhances accuracy while reducing the number of classifiers used, employing convex relaxation techniques to efficiently optimize ensemble weights.

Contribution

The novel SDWEC method models ensemble weight optimization as a non-convex cost function with sparsity, solved efficiently through convex relaxation, improving accuracy and reducing testing time.

Findings

SDWEC achieves better or comparable accuracy to state-of-the-art methods.

SDWEC reduces the number of classifiers needed, decreasing testing time.

Validated on 11 datasets, showing consistent performance improvements.

Abstract

In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC) that improves classification accuracy and minimizes the number of classifiers is proposed. Using pre-trained classifiers, an ensemble in which base classifiers votes according to assigned weights is formed. These assigned weights directly affect classifier accuracy. In the proposed method, ensemble weights finding problem is modeled as a cost function with the 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. As the proposed cost function is non-convex thus hard to solve, convex relaxation techniques and novel approximations are employed to obtain a numerically efficient solution. Sparsity term of cost function allows trade-off between accuracy and testing time when needed. The efficiency of SDWEC was tested on 11 datasets and compared with the state-of-the art classifier ensemble methods. The results show that SDWEC provides better or similar accuracy levels using fewer classifiers and reduces testing time for ensemble.