Aggregation of classifiers: a justifiable information granularity approach.

Nguyen, Tien Thanh; Pham, Xuan Cuong; Liew, Alan Wee-Chung; Pedrycz, Witold

Abstract

In this paper, we introduced a new approach of combining multiple classifiers in a heterogeneous ensemble system. Instead of using numerical membership values when combining, we constructed interval membership values for each class prediction from the meta-data of observation by using the concept of information granule. In the proposed method, the uncertainty (diversity) of the predictions produced by the base classifiers is quantified by the interval-based information granules. The decision model is then generated by considering both bound and length of the intervals. Extensive experimentation using the UCI datasets has demonstrated the superior performance of our algorithm over other algorithms including six fixed combining methods, one trainable combining method, AdaBoost, bagging, and random subspace.

Citation

NGUYEN, T.T., PHAM, X.C., LIEW, A.W.-C. and PEDRYCZ, W. 2019. Aggregation of classifiers: a justifiable information granularity approach. IEEE transactions on cybernetics [online], 49(6), pages 2168-2177. Available from: https://doi.org/10.1109/TCYB.2018.2821679

• Journal Article Type: Article
• Acceptance Date: Mar 27, 2018
• Online Publication Date: Apr 16, 2018
• Publication Date: Jun 30, 2019
• Deposit Date: Feb 14, 2022
• Publicly Available Date: Feb 14, 2022
• Journal: IEEE Transactions on Cybernetics
• Print ISSN: 2168-2267
• Electronic ISSN: 2168-2275
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 49
• Issue: 6
• Pages: 2168-2177
• DOI: https://doi.org/10.1109/TCYB.2018.2821679
• Keywords: Ensemble method; Information granule; Information uncertainty; Justifiable granularity; Multiclassifiers system
• Public URL: https://rgu-repository.worktribe.com/output/983819