Feature Fusion for Improved Classification: Combining Dempster-Shafer Theory and Multiple CNN Architectures

• URL: http://arxiv.org/abs/2405.20230v1
• Date: Thu, 23 May 2024 20:44:10 GMT
• Title: Feature Fusion for Improved Classification: Combining Dempster-Shafer Theory and Multiple CNN Architectures
• Authors: Ayyub Alzahem, Wadii Boulila, Maha Driss, Anis Koubaa,
• Abstract summary: This paper introduces a novel algorithm leveraging Dempster-Shafer Theory (DST) to integrate multiple pre-trained models to form an ensemble capable of providing more reliable and enhanced classifications. Several experiments have been conducted on CIFAR-10 and CIFAR-100 datasets, demonstrating superior classification accuracy of the proposed DST-based method.
• Score: 1.0678175996321808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Addressing uncertainty in Deep Learning (DL) is essential, as it enables the development of models that can make reliable predictions and informed decisions in complex, real-world environments where data may be incomplete or ambiguous. This paper introduces a novel algorithm leveraging Dempster-Shafer Theory (DST) to integrate multiple pre-trained models to form an ensemble capable of providing more reliable and enhanced classifications. The main steps of the proposed method include feature extraction, mass function calculation, fusion, and expected utility calculation. Several experiments have been conducted on CIFAR-10 and CIFAR-100 datasets, demonstrating superior classification accuracy of the proposed DST-based method, achieving improvements of 5.4% and 8.4%, respectively, compared to the best individual pre-trained models. Results highlight the potential of DST as a robust framework for managing uncertainties related to data when applying DL in real-world scenarios.

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