Summary of Feature Fusion For Improved Classification: Combining Dempster-shafer Theory and Multiple Cnn Architectures, by Ayyub Alzahem and Wadii Boulila and Maha Driss and Anis Koubaa
Feature Fusion for Improved Classification: Combining Dempster-Shafer Theory and Multiple CNN Architectures
by Ayyub Alzahem, Wadii Boulila, Maha Driss, Anis Koubaa
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel algorithm introduced in this paper integrates multiple pre-trained Deep Learning (DL) models using Dempster-Shafer Theory (DST), enabling more reliable and enhanced classifications. The proposed method involves feature extraction, mass function calculation, fusion, and expected utility calculation. By leveraging DST, the ensemble demonstrates superior classification accuracy on CIFAR-10 and CIFAR-100 datasets, achieving improvements of 5.4% and 8.4%, respectively, over individual pre-trained models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make better predictions by combining multiple AI models using a special theory called Dempster-Shafer Theory. Imagine you’re trying to decide what’s in a picture – the algorithm takes features from the picture, calculates how sure it is about each feature, and then combines them to make a more informed decision. |
Keywords
» Artificial intelligence » Classification » Deep learning » Feature extraction
