Summary of Learning Discretized Bayesian Networks with Gomea, by Damy M.f. Ha et al.
Learning Discretized Bayesian Networks with GOMEA
by Damy M.F. Ha, Tanja Alderliesten, Peter A.N. Bosman
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 Bayesian networks model relationships between random variables under uncertainty and can be used to predict the likelihood of events and outcomes while incorporating observed evidence. This paper proposes an extension to an existing state-of-the-art structure learning approach called Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA), which jointly learns variable discretizations. The proposed Discretized Bayesian Network GOMEA (DBN-GOMEA) obtains similar or better results than the current state-of-the-art when tasked to retrieve randomly generated ground-truth networks. Moreover, this approach enables incorporating expert knowledge in a uniquely insightful fashion, finding multiple DBNs that trade-off complexity, accuracy, and the difference with a pre-determined expert network. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bayesian networks are special kinds of models that help us understand how things are related when we don’t know everything. This paper makes it possible to learn these models from data and also figure out how to turn numbers into simple categories. It’s like trying to find the best way to group people by height or weight. The new approach is really good at finding the right model and can even use expert knowledge to make it better. This could be important for things like medical diagnosis or predicting what might happen in a situation. |
Keywords
* Artificial intelligence * Bayesian network * Likelihood
