Enabling Large-Scale Bayesian Network Learning by Preserving Intercluster Directionality

We propose a recursive clustering and order restriction (R-CORE) method for learning large-scale Bayesian networks. The proposed method considers a reduced search space for directed acyclic graph (DAG) structures in scoring-based Bayesian network learning. The candidate DAG structures are restricted by clustering variables and determining the intercluster directionality. The proposed method considers cycles on only cmax(< n) variables rather than on all n variables for DAG structures. The R-CORE method could be a useful tool in very large problems where only a very small amount of training data is available.

[1]  Joe Suzuki,et al.  Learning Bayesian Belief Networks Based on the MDL Principle : An Efficient Algorithm Using the Branch and Bound Technique , 1999 .

[2]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[3]  David Maxwell Chickering,et al.  Learning Bayesian Networks is , 1994 .

[4]  Pedro Larrañaga,et al.  Analysis of the behaviour of genetic algorithms when learning Bayesian network structure from data , 1997, Pattern Recognit. Lett..

[5]  C S Jensen,et al.  Blocking Gibbs sampling for linkage analysis in large pedigrees with many loops. , 1999, American journal of human genetics.

[6]  Ewart R. Carson,et al.  A Model-Based Approach to Insulin Adjustment , 1991, AIME.

[7]  Nir Friedman,et al.  Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm , 1999, UAI.

[8]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[9]  Gregory F. Cooper,et al.  The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks , 1989, AIME.

[10]  D. Heckerman,et al.  Toward Normative Expert Systems: Part I The Pathfinder Project , 1992, Methods of Information in Medicine.

[11]  Holger H. Hoos,et al.  Inference of Transcriptional Regulation Relationships from Gene Expression Data , 2003, Bioinform..

[12]  A. H. Murphy,et al.  Hailfinder: A Bayesian system for forecasting severe weather , 1996 .

[13]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[14]  Gregory F. Cooper,et al.  An Entropy-driven System for Construction of Probabilistic Expert Systems from Databases , 1990, UAI.

[15]  Silvia ACIDDepto.  BENEDICT : An Algorithm for Learning Probabilistic Belief Networks , 2007 .

[16]  Constantin F. Aliferis,et al.  A Novel Algorithm for Scalable and Accurate Bayesian Network Learning , 2004, MedInfo.

[17]  Byoung-Tak Zhang,et al.  Construction of Large-Scale Bayesian Networks by Local to Global Search , 2002, PRICAI.

[18]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[19]  Peter Grünwald,et al.  A tutorial introduction to the minimum description length principle , 2004, ArXiv.

[20]  Doheon Lee,et al.  Modularized learning of genetic interaction networks from biological annotations and mRNA expression data , 2005, Bioinform..

[21]  J. Suzuki.  Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: Basic Properties , 1999 .