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Ensemble classifier generation using class-pure cluster balancing

conference contribution
posted on 16.03.2020, 00:00 by Muhammad Zohaib Jan, Brijesh Verma
Clustering based ensemble of classifiers have shown a significant improvement in classification accuracy in many real-world applications. Most of the existing clustering-based ensemble approaches generate and use predefined number of data clusters. However, datasets have different spatial structure that depends on number of characteristics for example class labels. Therefore, using a predefined set of hyperparameters to generate a clustering-based ensemble classifier is not an effective methodology. In this paper we propose a methodology to overcome this limitation by generating dataset dependent strong and balanced data clusters per class. This ensures that any spatial information that is inherent in the dataset can be exploited to train an ensemble classifier that can surpass the classification accuracy plateau. An ensemble classifier framework is proposed that benefits from this methodology and trains base classifiers on generated strong and balanced data clusters. We have evaluated the proposed approach on 8 benchmark datasets from UCI repository. Detailed experiments and results are presented in the paper, and it is evident from the results that varying the number of clusters per class does have an impact on the overall classification accuracy of the ensemble. © Springer Nature Switzerland AG 2019.

Funding

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

History

Editor

Gedeon TDT; Wong KW; Lee M

Volume

1143 CCIS

Start Page

761

End Page

769

Number of Pages

9

Start Date

12/12/2019

Finish Date

15/12/2019

eISSN

1865-0937

ISSN

1865-0929

ISBN-13

9783030368012

Location

Sydney, NSW, Australia

Publisher

Springer

Place of Publication

Cham, Switzerland

Peer Reviewed

Yes

Open Access

No

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Name of Conference

26th International Conference on Neural Information Processing (ICONIP 2019)

Exports