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Deep Convolutional Neural Network Ensembles Using ECOC
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DOI：10.1109/ACCESS.2021.3088717.png)
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Abstract
En 中文
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.
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
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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