Abstract
Inductive Logic Programming (ILP) is an efficient technique for relational data mining, but when ILP is applied in imperfect domains, the rules induced by ILP often struggle with the overfitting problem. This paper proposes a method to learn first-order Bayesian network (FOBN) which can handle imperfect data powerfully. Due to a high computation cost for directly learning FOBN, we adapt an ILP and a Bayesian network learner to construct FOBN. We propose a feature extraction algorithm to generate features from ILP rules, and use these features as the main structure of the FOBN. We also propose a propositionalisation algorithm for translating the original data into the single table format to learn the remaining parts of the FOBN structure and its conditional probability tables by a standard Bayesian network learner.
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Chatpatanasiri, R., Kijsirikul, B. (2003). Upgrading ILP Rules to First-Order Bayesian Networks. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8_59
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DOI: https://doi.org/10.1007/3-540-36175-8_59
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