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Evolving interval-based representation for multiple classifier fusion.

Nguyen, Tien Thanh; Dang, Manh Truong; Baghel, Vimal Anand; Luong, Anh Vu; McCall, John; Liew, Alan Wee-Chung


Vimal Anand Baghel

Anh Vu Luong

Alan Wee-Chung Liew


Designing an ensemble of classifiers is one of the popular research topics in machine learning since it can give better results than using each constituent member. Furthermore, the performance of ensemble can be improved using selection or adaptation. In the former, the optimal set of base classifiers, meta-classifier, original features, or meta-data is selected to obtain a better ensemble than using the entire classifiers and features. In the latter, the base classifiers or combining algorithms working on the outputs of the base classifiers are made to adapt to a particular problem. The adaptation here means that the parameters of these algorithms are trained to be optimal for each problem. In this study, we propose a novel evolving combining algorithm using the adaptation approach for the ensemble systems. Instead of using numerical value when computing the representation for each class, we propose to use the interval-based representation for the class. The optimal value of the representation is found through Particle Swarm Optimization. During classification, a test instance is assigned to the class with the interval-based representation that is closest to the base classifiers’ prediction. Experiments conducted on a number of popular dataset confirmed that the proposed method is better than the well-known ensemble systems using Decision Template and Sum Rule as combiner, L2-loss Linear Support Vector Machine, Multiple Layer Neural Network, and the ensemble selection methods based on GA-Meta-data, META-DES, and ACO.


NGUYEN, T.T., DANG,M.T., BAGHEL, V.A., LUONG, A.V., MCCALL, J. and LIEW, A.W.-C. 2020 Evolving interval-based representation for multiple classifier fusion. Knowledge-based systems [online], 201-202, article ID 106034. Available from:

Journal Article Type Article
Acceptance Date May 13, 2020
Online Publication Date May 16, 2020
Publication Date Aug 9, 2020
Deposit Date May 15, 2020
Publicly Available Date May 17, 2021
Journal Knowledge-based systems
Print ISSN 0950-7051
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 201-202
Article Number 106034
Keywords Ensemble method; Multiple classifiers; Classifiers fusion; Combining classifiers; Ensemble system
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