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Cluster-oriented ensemble classifier : impact of multi-cluster characterisation on ensemble classifier learning

journal contribution
posted on 06.12.2017, 00:00 by Brijesh Verma, Ashfaqur Rahman
This paper presents a novel cluster oriented ensemble classifier. The proposed ensemble classifier is based on original concepts such as learning of cluster boundaries by the base classifiers and mapping of cluster confidences to class decision using a fusion classifier. The categorised data set is characterised into multiple clusters and fed to a number of distinctive base classifiers. The base classifiers learn cluster boundaries and produce cluster confidence vectors. A second level fusion classifier combines the cluster confidences and maps to class decisions. The proposed ensemble classifier modifies the learning domain for the base classifiers and facilitates efficient learning. The proposed approach is evaluated on benchmark data sets from UCI machine learning repository to identify the impact of multi-cluster boundaries on classifier learning and classification accuracy. The experimental results and two–tailed sign test demonstrate the superiority of the proposed cluster oriented ensemble classifier over existing ensemble classifiers published in the literature.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

24

Issue

4

Start Page

605

End Page

618

Number of Pages

14

ISSN

1041-4347

Location

USA

Publisher

IEEE

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Centre for Intelligent and Networked Systems (CINS); Faculty of Arts, Business, Informatics and Education; Institute for Resource Industries and Sustainability (IRIS);

Era Eligible

Yes

Journal

IEEE transactions on knowledge and data engineering.