A Method for Hybrid Bayesian Network Structure Learning from Massive Data Using MapReduce

Bayesian Network is the popular and important data mining model for representing uncertain knowledge. For large scale data it is often too costly to learn the accurate structure. To resolve this problem, much work has been done on migrating the structure learning algorithms to the MapReduce framework. In this paper, we introduce a distributed hybrid structure learning algorithm by combining the advantages of constraint-based and score-and-search-based algorithms. By reusing the intermediate results of MapReduce, the algorithm greatly simplified the computing work and got good results in both efficiency and accuracy.

[1]  David Page,et al.  Learning Bayesian Network Structure from Correlation-Immune Data , 2007, UAI.

[2]  David Sontag,et al.  SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure , 2013, UAI.

[3]  Yang Xiang,et al.  Acquisition of Causal Models for Local Distributions in Bayesian Networks , 2014, IEEE Transactions on Cybernetics.

[4]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[5]  Jose Miguel Puerta,et al.  Structural Learning of Bayesian Networks Via Constrained Hill Climbing Algorithms: Adjusting Trade‐off between Efficiency and Accuracy , 2015, Int. J. Intell. Syst..

[6]  Wei Chen,et al.  Massively parallel learning of Bayesian networks with MapReduce for factor relationship analysis , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[7]  Thomas Hofmann,et al.  Map-Reduce for Machine Learning on Multicore , 2007 .

[8]  Gregory F. Cooper,et al.  A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .

[9]  Shaohua Tan,et al.  Incremental learning Bayesian network structures efficiently , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[10]  Andrés Cano,et al.  A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Keke Gai,et al.  An Empirical Study on Preprocessing High-Dimensional Class-Imbalanced Data for Classification , 2015, 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems.

[12]  Qiang Ji,et al.  Efficient Structure Learning of Bayesian Networks using Constraints , 2011, J. Mach. Learn. Res..

[13]  James Cussens,et al.  Bayesian network learning by compiling to weighted MAX-SAT , 2008, UAI.

[14]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[15]  Hui Liu,et al.  A new hybrid method for learning bayesian networks: Separation and reunion , 2017, Knowl. Based Syst..

[16]  Keke Gai,et al.  A Classification Algorithm Based on Ensemble Feature Selections for Imbalanced-Class Dataset , 2016, 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS).

[17]  Doug Fisher,et al.  Learning from Data: Artificial Intelligence and Statistics V , 1996 .

[18]  Constantin F. Aliferis,et al.  The max-min hill-climbing Bayesian network structure learning algorithm , 2006, Machine Learning.

[19]  Weiyi Liu,et al.  A MapReduce-Based Method for Learning Bayesian Network from Massive Data , 2013, APWeb.

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

[21]  Tom White,et al.  Hadoop: The Definitive Guide , 2009 .

[22]  Anders L. Madsen,et al.  A parallel algorithm for Bayesian network structure learning from large data sets , 2017, Knowl. Based Syst..

[23]  Weiyi Liu,et al.  A Parallel and Incremental Approach for Data-Intensive Learning of Bayesian Networks , 2015, IEEE Transactions on Cybernetics.

[24]  David A. Bell,et al.  Learning Bayesian networks from data: An information-theory based approach , 2002, Artif. Intell..

[25]  Haytham Elghazel,et al.  A hybrid algorithm for Bayesian network structure learning with application to multi-label learning , 2014, Expert Syst. Appl..

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

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