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Combining heterogeneous classifiers via granular prototypes.

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


Tien Thanh Nguyen

Mai Phuong Nguyen

Xuan Cuong Pham

Alan Wee-Chung Liew

Witold Pedrycz


In this study, a novel framework to combine multiple classifiers in an ensemble system is introduced. Here we exploit the concept of information granule to construct granular prototypes for each class on the outputs of an ensemble of base classifiers. In the proposed method, uncertainty in the outputs of the base classifiers on training observations is captured by an interval-based representation. To predict the class label for a new observation, we first determine the distances between the output of the base classifiers for this observation and the class prototypes, then the predicted class label is obtained by choosing the label associated with the shortest distance. In the experimental study, we combine several learning algorithms to build the ensemble system and conduct experiments on the UCI, colon cancer, and selected CLEF2009 datasets. The experimental results demonstrate that the proposed framework outperforms several benchmarked algorithms including two trainable combining methods, i.e., Decision Template and Two Stages Ensemble System, AdaBoost, Random Forest, L2-loss Linear Support Vector Machine, and Decision Tree.

Journal Article Type Article
Publication Date Dec 31, 2018
Journal Applied soft computing
Print ISSN 1568-4946
Electronic ISSN 1872-9681
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 73
Pages 795-815
Institution Citation NGUYEN, T.T., NGUYEN, M.P., PHAM, X.C., LIEW, A. W.-C. and PEDRYCZ, W. 2018. Combining heterogeneous classifiers via granular prototypes. Applied soft computing [online], 73, pages 795-815. Available from:
Keywords Ensemble method; Multiple classifiers system; Information granule; Information uncertainty; Supervised learning


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