Sparsity-driven weighted ensemble classifier

DOI

10.2991/ijcis.11.1.73How to use a DOI?

Keywords

Machine Learning; Ensemble; Convex Relaxation; Classification; Classifier Ensembles

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.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Atilla Özgür
AU  - Fatih Nar
AU  - Hamit Erdem
PY  - 2018
DA  - 2018/04/20
TI  - Sparsity-driven weighted ensemble classifier
JO  - International Journal of Computational Intelligence Systems
SP  - 962
EP  - 978
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.73
DO  - 10.2991/ijcis.11.1.73
ID  - Özgür2018
ER  -