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A homogeneous-heterogeneous ensemble of classifiers.

Luong, Anh Vu; Vu, Trung Hieu; Nguyen, Phuong Minh; Van Pham, Nang; McCall, John; Liew, Alan Wee-Chung; Nguyen, Tien Thanh

Authors

Anh Vu Luong

Trung Hieu Vu

Phuong Minh Nguyen

Nang Van Pham

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Professor John McCall j.mccall@rgu.ac.uk
Professorial Lead (NSC) & Lead of the Computational Intelligence Research Group

Alan Wee-Chung Liew



Contributors

Haiqin Yang
Editor

Kitsuchart Pasupa
Editor

Andrew Chi-Sing Leung
Editor

James T. Kwok
Editor

Irwin King
Editor

Jonathan H. Chan
Editor

Abstract

In this study, we introduce an ensemble system by combining homogeneous ensemble and heterogeneous ensemble into a single framework. Based on the observation that the projected data is significantly different from the original data as well as each other after using random projections, we construct the homogeneous module by applying random projections on the training data to obtain the new training sets. In the heterogeneous module, several learning algorithms will train on the new training sets to generate the base classifiers. We propose four combining algorithms based on Sum Rule and Majority Vote Rule for the proposed ensemble. Experiments on some popular datasets confirm that the proposed ensemble method is better than several well-known benchmark algorithms proposed framework has great flexibility when applied to real-world applications. The proposed framework has great flexibility when applied to real-world applications by using any techniques that make rich training data for the homogeneous module, as well as using any set of learning algorithms for the heterogeneous module.

Citation

LUONG, A.V., VU, T.H., NGUYEN, P.M., VAN PHAM, N., MCCALL, J., LIEW, A.W.-C. and NGUYEN, T.T. 2020. A homogeneous-heterogeneous ensemble of classifiers. In Yang, H., Pasupa, K., Leung, A.C.-S., Kwok, J.T., Chan, J.H. and King, I. (eds.) Neural information processing: proceedings of 27th International conference on neural information processing 2020 (ICONIP 2020), part 5. Communications in computer and information science, 1333. Cham: Springer [online], pages, 251-259. Available from: https://doi.org/10.1007/978-3-030-63823-8_30

Conference Name 27th International conference on neural information processing 2020 (ICONIP 2020)
Conference Location Bangkok, Thailand [virtual conference]
Start Date Nov 18, 2020
End Date Dec 22, 2020
Acceptance Date Aug 15, 2020
Online Publication Date Nov 17, 2020
Publication Date Dec 31, 2020
Deposit Date Dec 15, 2020
Publicly Available Date Dec 15, 2020
Publisher Springer
Pages 251-259
Series Title Communications in computer and information science
Series Number 1333
Series ISSN 1865-0929
Book Title Neural information processing: proceedings of 27th International conference on neural information processing 2020 (ICONIP 2020), Bangkok, Thailand, November 18-22, 2020, part V
ISBN 9783030638221
DOI https://doi.org/10.1007/978-3-030-63823-8_30
Keywords Ensemble method; Multiple classifiers; Combining classifiers; Random projection; Ensemble learning; Combining methods
Public URL https://rgu-repository.worktribe.com/output/1005529

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