Learning Bayesian network equivalence classes with ant colony optimization

Ronan Daly, Qiang Shen

Research output: Contribution to journalArticlepeer-review

58 Citations (SciVal)
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Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm called ACO-E, to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and implemented. Firstly, multiple types of moves are allowed. Secondly, moves can be given in terms of indices that are not based on construction graph nodes. The results of testing show that ACO-E performs better than a greedy search and other state-of-the-art and metaheuristic algorithms whilst searching in the space of equivalence classes.
Original languageEnglish
Pages (from-to)391-447
Number of pages57
JournalJournal of Artificial Intelligence Research
Publication statusPublished - Jun 2009


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