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Learning-Based confidence estimation for Multi-modal classifier fusion

Nadeem, U., Bennamoun, M., Sohel, F. and Togneri, R. (2019) Learning-Based confidence estimation for Multi-modal classifier fusion. Lecture Notes in Computer Science, 11954 . pp. 299-312.

Link to Published Version: https://doi.org/10.1007/978-3-030-36711-4_26
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Abstract

We propose a novel confidence estimation method for predictions from a multi-class classifier. Unlike existing methods, we learn a confidence-estimator on the basis of a held-out set from the training data. The predicted confidence values by the proposed system are used to improve the accuracy of multi-modal emotion and sentiment classification. The scores of different classes from the individual modalities are superposed on the basis of confidence values. Experimental results demonstrate that the accuracy of the proposed confidence based fusion method is significantly superior to that of the classifier trained on any modality separately, and achieves superior performance compared to other fusion methods.

Item Type: Journal Article
Murdoch Affiliation: Information Technology, Mathematics and Statistics
Publisher: Springer Verlag
Copyright: © 2019, Springer Nature Switzerland AG.
Other Information: Conference paper: 26th International Conference on Neural Information Processing, ICONIP 2019; Sydney; Australia; 12 December 2019 through 15 December 2019
URI: http://researchrepository.murdoch.edu.au/id/eprint/53990
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