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

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

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 27th International conference on neural information processing 2020 (ICONIP 2020)
• 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
• Peer Reviewed: Peer Reviewed
• 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