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A novel ensemble classifier approach using weak classifier learning on overlapping clusters

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conference contribution
posted on 06.12.2017, 00:00 by Ashfaqur RahmanAshfaqur Rahman, Brijesh VermaBrijesh Verma
This paper presents a novel approach for creating and training of an ensemble classifier. The approach is based on creating atomic and non-atomic clusters at different levels, training of weak classifiers on overlapping clusters and fusion of their decisions. The subsets of data are obtained by clustering of original training data sets into multiple partitions. As each partition represents highly correlated patterns from different classes, the proposed approach trains weak classifiers on difficult–to–classify patterns and combines the decision at various levels. The approach is tested on six benchmark datasets from UCI machine learning repository. The results show that the proposed approach achieves better classification accuracy than the existing approaches.

Funding

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

History

Start Page

328

End Page

334

Number of Pages

7

Start Date

01/01/2010

Finish Date

01/01/2010

ISBN-13

9781424469178

Location

Barcelona, Spain

Publisher

IEEE

Place of Publication

USA

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

Name of Conference

IEEE World Congress on Computational Intelligence;IEEE International Joint Conference on Neural Networks