Summary of Classification with a Network Of Partially Informative Agents: Enabling Wise Crowds From Individually Myopic Classifiers, by Tong Yao et al.
Classification with a Network of Partially Informative Agents: Enabling Wise Crowds from Individually Myopic Classifiers
by Tong Yao, Shreyas Sundaram
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA)
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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 classification algorithm is proposed for a network of heterogeneous agents that receive local data and can only distinguish between a subset of classes. The algorithm iteratively updates each agent’s belief on all possible classes based on its local signals and neighbor’s beliefs, using posterior probabilities from the local classifier. A distributed min-rule is adopted to update the global belief and enable learning of the true class for all agents. Under certain assumptions, the algorithm converges to the true class asymptotically almost surely, with a demonstrated performance in simulation experiments using image data and random forest classifiers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A group of different “learners” (like computers or people) tries to figure out what’s going on in the world. Each learner has some information about what might be happening, but they can only understand part of it. The learners share their ideas with each other, which helps them learn more. A special way is developed to make sure all the learners get closer to understanding the truth. This works really well and makes it possible for all the learners to figure out what’s going on. |
Keywords
» Artificial intelligence » Classification » Random forest
