UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Deep Convolutional Neural Network Ensembles Using ECOC

Ahmed, SAA; Zor, C; Yanikoglu, BA; Awais, M; Kittler, J; (2020) Deep Convolutional Neural Network Ensembles Using ECOC. IEEE Access , 9 pp. 86083-86095. 10.1109/ACCESS.2021.3088717. Green open access

[thumbnail of 09452167.pdf]
Preview
Text
09452167.pdf - Published version

Download (1MB) | Preview

Abstract

Deep neural networks have enhanced the performance of decision making systems in many applications, including image understanding, and further gains can be achieved by constructing ensembles. However, designing an ensemble of deep networks is often not very beneficial since the time needed to train the networks is generally very high or the performance gain obtained is not very significant. In this paper, we analyse an error correcting output coding (ECOC) framework for constructing ensembles of deep networks and propose different design strategies to address the accuracy-complexity trade-off. We carry out an extensive comparative study between the introduced ECOC designs and the state-of-the-art ensemble techniques such as ensemble averaging and gradient boosting decision trees. Furthermore, we propose a fusion technique, that is shown to achieve the highest classification performance.

Type: Article
Title: Deep Convolutional Neural Network Ensembles Using ECOC
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ACCESS.2021.3088717
Publisher version: http://doi.org/10.1109/access.2021.3088717
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Keywords: Deep learning, ensemble learning, error correcting output coding, gradient boosting decision trees, multi-task classification
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10130499
Downloads since deposit
16Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item