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Structural-EM for learning PDG models from incomplete data

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2010年第5期51卷 515-530页

作者： Nielsen, Jens D. Rumi, Rafael Salmeron, Antonio Univ Castilla La Mancha Dept Comp Sci Albacete 02071 Spain Univ Almeria Dept Appl Math & Stat Almeria 04120 Spain

Probabilistic Decision Graphs (PDGs) are a class of graphical models that can naturally encode some context specific independencies that cannot always be efficiently captured by other popular models, such as Bayesian Networks. Furthermore, inference can be carried out efficiently over a PDG, in time linear in the size of the model. The problem of learning PDGs from data has been studied in the literature, but only for the case of complete data. We propose an algorithm for learning PDGs in the presence of missing data. The proposed method is based on the Expectation-Maximisation principle for estimating the structure of the model as well as the parameters. We test our proposal on both artificially generated data with different rates of missing cells and real incomplete data. We also compare the PDG models learnt by our approach to the commonly used Bayesian Network (BN) model. The results indicate that the PDG model is less sensitive to the rate of missing data than BN model. Also, though the BN models usually attain higher likelihood, the PDGs are close to them also in size, which makes the learnt PDGs preferable for probabilistic inference purposes. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Machine learning Graphical models learning from incomplete data

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Structural-EM for learning PDG models from incomplete data

Structural-EM for learning PDG models from incomplete data

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4th European Workshop on Probabilistic Graphical Models

作者： Nielsen, Jens D. Rumi, Rafael Salmeron, Antonio Univ Castilla La Mancha Dept Comp Sci Albacete 02071 Spain Univ Almeria Dept Appl Math & Stat Almeria 04120 Spain

关键词： Machine learning Graphical models learning from incomplete data

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learning Bayesian networks for clustering by means of constructive induction

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PATTERN RECOGNITION LETTERS 1999年第11-13期20卷 1219-1230页

作者： Peña, JM Lozano, JA Larrañaga, P Univ Basque Country Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp E-20080 San Sebastian Spain

The purpose of this paper is to present and evaluate a heuristic algorithm for learning Bayesian networks for clustering. Our approach is based upon improving the Naive-Bayes model by means of constructive induction. A key idea in this approach is to treat expected data as real data. This allows us to complete the database and to take advantage of factorable closed forms for the marginal likelihood. In order to get such an advantage, we search for parameter values using the EM algorithm or another alternative approach that we have developed: a hybridization of the Bound and Collapse method and the EM algorithm, which results in a method that exhibits a faster convergence rate and a more effective behaviour than the EM algorithm. Also, we consider the possibility of interleaving runnings of these two methods after each structural change. We evaluate our approach on synthetic and real-world databases. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： clustering Bayesian networks learning from incomplete data constructive induction EM algorithm bound and collapse method simulated annealing

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