A Homogeneous-Heterogeneous Ensemble of Classifiers

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Luong, AV
Vu, TH
Nguyen, PM
Van Pham, N
McCall, J
Liew, AWC
Nguyen, TT
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2020
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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.

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Communications in Computer and Information Science
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© Springer Nature Switzerland AG 2020. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
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Subject
Evolutionary computation
Fuzzy computation
Procedural content generation
Deep learning
Neural networks
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Luong, AV; Vu, TH; Nguyen, PM; Van Pham, N; McCall, J; Liew, AWC; Nguyen, TT, A Homogeneous-Heterogeneous Ensemble of Classifiers, Communications in Computer and Information Science, 2020, 1333, pp. 251-259