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VEGAS: a variable length-based genetic algorithm for ensemble selection in deep ensemble learning.

Han, Kate; Pham, Tien; Vu, Trung Hieu; Dang, Truong; McCall, John; Nguyen, Tien Thanh

Authors

Tien Pham

Trung Hieu Vu

Truong Dang

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



Contributors

Ngoc Thanh Nguyen
Editor

Suphamit Chittayasothorn
Editor

Dusit Niyato
Editor

Bogdan Trawiński
Editor

Abstract

In this study, we introduce an ensemble selection method for deep ensemble systems called VEGAS. The deep ensemble models include multiple layers of the ensemble of classifiers (EoC). At each layer, we train the EoC and generates training data for the next layer by concatenating the predictions for training observations and the original training data. The predictions of the classifiers in the last layer are combined by a combining method to obtain the final collaborated prediction. We further improve the prediction accuracy of a deep ensemble model by searching for its optimal configuration, i.e., the optimal set of classifiers in each layer. The optimal configuration is obtained using the Variable-Length Genetic Algorithm (VLGA) to maximize the prediction accuracy of the deep ensemble model on the validation set. We developed three operators of VLGA: roulette wheel selection for breeding, a chunk-based crossover based on the number of classifiers to generate new offsprings, and multiple random points-based mutation on each offspring. The experiments on 20 datasets show that VEGAS outperforms selected benchmark algorithms, including two well-known ensemble methods (Random Forest and XgBoost) and three deep learning methods (Multiple Layer Perceptron, gcForest, and MULES).

Citation

HAN, K., PHAM, T., VU, T.H., DANG, T., MCCALL, J. and NGUYEN, T.T. 2021. VEGAS: a variable length-based genetic algorithm for ensemble selection in deep ensemble learning. In Nguyen, N.T., Chittayasothorn, S., Niyato, D. and Trawiński, B. (eds.) Intelligent information and database systems: proceedings of the 13th Asian conference on intelligent information and database systems 2021 (ACCIIDS 2021), 7-10 April 2021, [virtual conference]. Lecture Notes in Computer Science, 12672. Cham: Springer [online], pages 168–180. Available from: https://doi.org/10.1007/978-3-030-73280-6_14

Conference Name 13th Asian conference on intelligent information and database systems 2021 (ACIIDS 2021)
Conference Location [virtual conference]
Start Date Apr 7, 2021
End Date Apr 10, 2021
Acceptance Date Dec 30, 2020
Online Publication Date Apr 5, 2021
Publication Date Dec 31, 2021
Deposit Date May 31, 2021
Publicly Available Date May 31, 2021
Publisher Springer
Pages 168-180
Series Title Lecture notes in computer science
Series Number 12672
Series ISSN 0302-9743
Book Title Intelligent information and database systems
ISBN 9783030732790
DOI https://doi.org/10.1007/978-3-030-73280-6_14
Keywords Deep learning; Ensemble learning; Ensemble selection; Classifier selection; Ensemble of classifiers; Genetic algorithum
Public URL https://rgu-repository.worktribe.com/output/1335449

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