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Deep heterogeneous ensemble.

Nguyen, Tien Thanh; Dang, Manh Truong; Pham, Tien Dung; Dao, Lan Phuong; Luong, Anh Vu; McCall, John; Liew, Alan Wee Chung


Tien Dung Pham

Lan Phuong Dao

Anh Vu Luong

Alan Wee Chung Liew


In recent years, deep neural networks (DNNs) have emerged as a powerful technique in many areas of machine learning. Although DNNs have achieved great breakthrough in processing images, video, audio and text, it also has some limitations such as needing a large number of labeled data for training and having a large number of parameters. Ensemble learning, meanwhile, provides a learning model by combining many different classifiers such that an ensemble of classifiers is better than using single classifier. In this study, we propose a deep ensemble framework called Deep Heterogeneous Ensemble (DHE) for supervised learning tasks. In each layer of our algorithm, the input data is passed through a feature selection method to remove irrelevant features and prevent overfitting. The cross-validation with K learning algorithms is applied to the selected data, in order to obtain the meta-data and the K base classifiers for the next layer. In this way, one layer will output the meta-data as the input data for the next layer, the base classifiers, and the indices of the selected meta-data. A combining algorithm is then applied on the meta-data of the last layer to obtain the final class prediction. Experiments on 30 datasets confirm that the proposed DHE is better than a number of well-known benchmark algorithms.


NGUYEN, T.T., DANG, M.T., PHAM, T.D., DAO, L.P., LUONG, A.V., MCCALL, J. and LIEW, A.W.C. 2019. Deep heterogeneous ensemble. Australian journal of intelligent information processing systems [online], 16(1): special issue on neural information processing: proceedings of the 26th International conference on neural information processing (ICONIP 2019), 12-15 December 2019, Sydney, Australia, pages 1-9. Available from:

Journal Article Type Conference Paper
Conference Name 26th International conference on neural information processing (ICONIP 2019)
Conference Location Sydney, Australia
Acceptance Date Sep 14, 2019
Online Publication Date Dec 31, 2019
Publication Date Dec 31, 2019
Deposit Date May 5, 2020
Publicly Available Date May 6, 2020
Journal Australian journal of intelligent information processing systems
Print ISSN 1321-2133
Electronic ISSN 1321-2133
Publisher Australian Journal of Intelligent Processing Systems
Peer Reviewed Peer Reviewed
Volume 16
Issue 1
Pages 1-9
Keywords Deep neural networks; Ensemble learning systems; Ensemble systems; Machine learning
Public URL
Publisher URL


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