Summary of Inter Observer Variability Assessment Through Ordered Weighted Belief Divergence Measure in Magdm Application to the Ensemble Classifier Feature Fusion, by Pragya Gupta (1) et al.
Inter Observer Variability Assessment through Ordered Weighted Belief Divergence Measure in MAGDM Application to the Ensemble Classifier Feature Fusion
by Pragya Gupta, Debjani Chakraborty, Debashree Guha
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Theory (cs.IT)
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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 A novel approach is presented in this study, which proposes an Evidential Multi-Attribute Group Decision-Making (MAGDM) method that addresses uncertainty and conflict among expert opinions. The framework consists of four main contributions: the generation of basic probability assignments to consider alternative characteristics, construction of ordered weighted belief and plausibility measures to capture intrinsic information, development of an ordered weighted belief divergence measure for final preference relationships, and demonstration through a real-world example. The Evidential MAGDM method is applied to ensemble classifier feature fusion in diagnosing retinal disorders using optical coherence tomography images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make group decisions is introduced in this study. It’s called Evidential Multi-Attribute Group Decision-Making (MAGDM). This approach helps when people with different opinions work together, but it also considers the uncertainty and conflicts that can happen. The method has four parts: making sure each alternative is considered fairly, using the experts’ opinions to get a sense of what’s important, figuring out how much support each group gets, and showing how this works in a real-life situation. |
Keywords
» Artificial intelligence » Probability
