Trusted Multi-View Classification With Dynamic Evidential Fusion

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Trusted Multi-View Classification With Dynamic Evidential Fusion
Title:
Trusted Multi-View Classification With Dynamic Evidential Fusion
Journal Title:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publication Date:
03 May 2022
Citation:
Han, Z., Zhang, C., Fu, H., & Zhou, J. T. (2023). Trusted Multi-View Classification With Dynamic Evidential Fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2), 2551–2566. https://doi.org/10.1109/tpami.2022.3171983
Abstract:
Existing multi-view classification algorithms focus on promoting accuracy by exploiting different views, typically integrating them into common representations for follow-up tasks. Although effective, it is also crucial to ensure the reliability of both the multi-view integration and the final decision, especially for noisy, corrupted and out-of-distribution data. Dynamically assessing the trustworthiness of each view for different samples could provide reliable integration. This can be achieved through uncertainty estimation. With this in mind, we propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC), providing a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. The proposed TMC can promote classification reliability by considering evidence from each view. Specifically, we introduce the variational Dirichlet to characterize the distribution of the class probabilities, parameterized with evidence from different views and integrated with the Dempster-Shafer theory. The unified learning framework induces accurate uncertainty and accordingly endows the model with both reliability and robustness against possible noise or corruption. Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AI3 HTPO Seed Fund
Grant Reference no. : C211118012

This research / project is supported by the A*STAR - AME Programmatic Funding Scheme
Grant Reference no. : A18A1b0045

This research is supported by core funding from: SERC Central Research Fund
Grant Reference no. :

This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB2101900, the National Natural Science Foundation of China (61976151, 61925602, 61732011)
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
0162-8828
2160-9292
1939-3539
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