Deep convolutional neural network ensembles using ECOC

Ali Ahmed, Sara Atito and Zor, Cemre and Awais, Muhammad and Yanıkoğlu, Berrin and Kittler, Josef (2021) Deep convolutional neural network ensembles using ECOC. IEEE Access, 9 . pp. 86083-86095. ISSN 2169-3536

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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.
Item Type: Article
Uncontrolled Keywords: Boosting, Training, Decision trees, Time complexity, Feature extraction, Vegetation, Deep learning, Deep learning, ensemble learning, error correcting output coding, gradient boosting decision trees, multi-task classification
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Berrin Yanıkoğlu
Date Deposited: 03 Sep 2021 15:58
Last Modified: 29 Aug 2022 21:14

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