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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 3/2019

13-10-2017 | Original Article

A dynamic selection ensemble method for target recognition based on clustering and randomized reference classifier

Authors: Xueman Fan, Shengliang Hu, Jingbo He

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2019

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Abstract

In order to improve the generalization ability and recognition efficiency of the maritime surveillance radar, a novel selection ensemble technique, termed KMRRC, based on k-medoids clustering and random reference classifier (RRC) is proposed. By disturbing the training set base classifiers are generated, which are then divided into several clusters based on pairwise diversity metrics, finally the RRC model is used to select several most competent classifiers from each cluster to classify each query object. The performance of KMRRC is compared against nine ensemble learning methods using a self-built high range resolution profile (HRRP) data set and twenty UCI databases. The experimental results clearly show the KMMRRC’s feasibility and effectiveness. In addition, the influence of the selection of diversity measures is studied concurrently.
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Metadata
Title
A dynamic selection ensemble method for target recognition based on clustering and randomized reference classifier
Authors
Xueman Fan
Shengliang Hu
Jingbo He
Publication date
13-10-2017
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0732-2

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