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Optimizing clustering to promote data diversity when generating an ensemble classifier

In this paper, we propose a method to generate an optimized ensemble classifier. In the proposed method, a diverse input space is created by clustering training data incrementally within a cycle. A cycle is one complete round that includes clustering, training, and error calculation. In each cycle, a random upper bound of clustering is chosen and data clusters are generated. A set of heterogeneous classifiers are trained on all generated clusters to promote structural diversity. An ensemble classifier is formed in each cycle and generalization error of that ensemble is calculated. This process is optimized to find the set of classifiers which can have the lowest generalization error. The process of optimization terminates when generalization error can no longer be minimized. The cycle with the lowest error is then selected and all trained classifiers of that particular cycle are passed to the next stage. Any classifier having lower accuracy than the average accuracy of the pool is discarded, and the remaining classifiers form the proposed ensemble classifier. The proposed ensemble classifier is tested on classification benchmark datasets from UCI repository. The results are compared with existing state-of-the-art ensemble classifier methods including Bagging and Boosting. It is demonstrated that the proposed ensemble classifier performs better than the existing ensemble methods. © 2018 Association for Computing Machinery.

Funding

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

History

Editor

Aguirre H; Takadama K

Start Page

1402

End Page

1409

Number of Pages

8

Start Date

15/07/2018

Finish Date

19/07/2018

ISBN-13

9781450357647

Location

Kyoto, Japan

Publisher

Association for Computer Machinery

Place of Publication

New York, NY

Peer Reviewed

Yes

Open Access

No

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Name of Conference

The Genetic and Evolutionary Computation Conference