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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Learning Diverse Bayesian Networks

February 1, 2023

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Authors

Cong Chen

City University of New York


Changhe Yuan

City University of New York


DOI:

10.1609/aaai.v33i01.33017793


Abstract:

Much effort has been directed at developing algorithms for learning optimal Bayesian network structures from data. When given limited or noisy data, however, the optimal Bayesian network often fails to capture the true underlying network structure. One can potentially address the problem by finding multiple most likely Bayesian networks (K-Best) in the hope that one of them recovers the true model. However, it is often the case that some of the best models come from the same peak(s) and are very similar to each other; so they tend to fail together. Moreover, many of these models are not even optimal respective to any causal ordering, thus unlikely to be useful. This paper proposes a novel method for finding a set of diverse top Bayesian networks, called modes, such that each network is guaranteed to be optimal in a local neighborhood. Such mode networks are expected to provide a much better coverage of the true model. Based on a globallocal theorem showing that a mode Bayesian network must be optimal in all local scopes, we introduce an A* search algorithm to efficiently find top M Bayesian networks which are highly probable and naturally diverse. Empirical evaluations show that our top mode models have much better diversity as well as accuracy in discovering true underlying models than those found by K-Best.

Topics: AAAI

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HOW TO CITE:

Cong Chen||Changhe Yuan Learning Diverse Bayesian Networks Proceedings of the AAAI Conference on Artificial Intelligence (2019) 7793-7800.

Cong Chen||Changhe Yuan Learning Diverse Bayesian Networks AAAI 2019, 7793-7800.

Cong Chen||Changhe Yuan (2019). Learning Diverse Bayesian Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 7793-7800.

Cong Chen||Changhe Yuan. Learning Diverse Bayesian Networks. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.7793-7800.

Cong Chen||Changhe Yuan. 2019. Learning Diverse Bayesian Networks. "Proceedings of the AAAI Conference on Artificial Intelligence". 7793-7800.

Cong Chen||Changhe Yuan. (2019) "Learning Diverse Bayesian Networks", Proceedings of the AAAI Conference on Artificial Intelligence, p.7793-7800

Cong Chen||Changhe Yuan, "Learning Diverse Bayesian Networks", AAAI, p.7793-7800, 2019.

Cong Chen||Changhe Yuan. "Learning Diverse Bayesian Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.7793-7800.

Cong Chen||Changhe Yuan. "Learning Diverse Bayesian Networks". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 7793-7800.

Cong Chen||Changhe Yuan. Learning Diverse Bayesian Networks. AAAI[Internet]. 2019[cited 2023]; 7793-7800.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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