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Multiple elimination of base classifiers in ensemble learning using accuracy and diversity comparisons

journal contribution
posted on 12.03.2021, 07:07 by Muhammad Zohaib JanMuhammad Zohaib Jan, Brijesh VermaBrijesh Verma
When generating ensemble classifiers, selecting the best set of classifiers from the base classifier pool is considered a combinatorial problem and an efficient classifier selection methodology must be utilized. Different researchers have used different strategies such as evolutionary algorithms, genetic algorithms, rule-based algorithms, simulated annealing, and so forth to select the best set of classifiers that can maximize overall ensemble classifier accuracy. In this article, we present a novel classifier selection approach to generate an ensemble classifier. The proposed approach selects classifiers in multiple rounds of elimination. In each round, a classifier is given a chance to be selected to become a part of the ensemble, if it can contribute to the overall ensemble accuracy or diversity; otherwise, it is put back into the pool. Each classifier is given multiple opportunities to participate in rounds of selection and they are discarded only if they have no remaining chances. The process is repeated until no classifier in the pool has any chance left to participate in the round of selection. To test the efficacy of the proposed approach, 13 benchmark datasets from the UCI repository are used and results are compared with single classifier models and existing state-of-the-art ensemble classifier approaches. Statistical significance testing is conducted to further validate the results, and an analysis is provided. © 2020 ACM.

Funding

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

History

Volume

11

Issue

6

Start Page

1

End Page

17

Number of Pages

17

eISSN

2157-6912

ISSN

2157-6904

Publisher

ACM

Language

en

Peer Reviewed

Yes

Open Access

No

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Journal

ACM Transactions on Intelligent Systems and Technology

Article Number

67