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Incremental Bayesian network structure learning in high dimensional domains

Amanullah Yasin, Philippe Leray
2013 2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)  
In this paper, we proposed an incremental algorithm for Bayesian network structure learning.  ...  Bayesian network structure learning A Bayesian network (BN) is a graphical representation of a probabilistic relationship among a set of random variables.  ... 
doi:10.1109/icmsao.2013.6552635 fatcat:5bvt6lavgjdlnpfetaybqty44q

An Order-based Algorithm for Learning Structure of Bayesian Networks

Shahab Behjati, Hamid Beigy
2018 European Workshop on Probabilistic Graphical Models  
In this paper, we study the problem learning structure of Bayesian networks from data.  ...  The problem of Bayesian networks structure learning (BNSL) takes a dataset as input and produces a directed acyclic graph (DAG) as the output.  ...  ACKNOWLEDGEMENTS The authors would like to thank James Cussens and Brandon Malone for useful comments for providing us using GOBNILP and URLearning softwares respectively.  ... 
dblp:conf/pgm/BehjatiB18 fatcat:y6a2j4h7gragfcgqxgfoul7h2q

A Novel Structure Learning Algorithm for Optimal Bayesian Network: Best Parents

Andrew Kreimer, Maya Herman
2016 Procedia Computer Science  
We present a novel algorithm for learning structure of a Bayesian Network.  ...  We provide a new greedy algorithm for optimal structure learning using conditional entropy. Also we perform a running time and performance comparison with other methods in the field.  ...  Best Parents Rational Best Parents is a novel approach for learning the structure of a Bayesian Network using greedy algorithm and a top down approach [4] .  ... 
doi:10.1016/j.procs.2016.08.092 fatcat:y3pe5jgh45gonksddevvyien2q

LSBN: A Large-Scale Bayesian Structure Learning Framework for Model Averaging [article]

Yang Lu, Mengying Wang, Menglu Li, Qili Zhu, Bo Yuan
2012 arXiv   pre-print
The motivation for this paper is to apply Bayesian structure learning using Model Averaging in large-scale networks.  ...  In comparison with other four state-of-art large-scale network structure learning algorithms such as ARACNE, PC, Greedy Search and MMHC, LSBN shows comparable results in five common benchmark datasets,  ...  In this paper, we propose a novel framework LSBN (Large-Scale Bayesian Network) to learn Bayesian structure for sufficiently large networks.  ... 
arXiv:1210.5135v1 fatcat:sd3ta7rkunedjci5pr74ebm44i

Page 5655 of Mathematical Reviews Vol. , Issue 2004g [page]

2004 Mathematical Reviews  
“The paper presents a novel algorithm overcoming this limi- tation for the tree-like class of Bayesian networks.  ...  “A well-known problem with Bayesian networks is the practical limitation on the number of variables for which a Bayesian network can be learned in reasonable time.  ... 

Structure Learning of Bayesian Network Using Swarm Intelligent Algorithm A Review

2022 Qalaai Zanist Scientific Journal  
Machines using Bayesian networks can be used to construct the framework of information in artificial intelligence that connects the variables in a probabilistic way.  ...  In the Enhanced Surface Water Searching Technique, most of the hunting for water is done by elephants during dry seasons. Pigeon Optimization, Simulated Annealing, Greedy Search, and the BDeu metrics  ...  Comparison of the Methods for structure learning Bayesian network 2 References Data Algorithms Results (Wang, J., & Liu, S.  ... 
doi:10.25212/lfu.qzj.7.1.38 fatcat:kytprw2rmfbzfcvdxqn6sv3uqy

An improved Bayesian network method for reconstructing gene regulatory network based on candidate auto selection

Linlin Xing, Maozu Guo, Xiaoyan Liu, Chunyu Wang, Lei Wang, Yin Zhang
2017 BMC Genomics  
Then based on CAS algorithm, we propose a globally optimal greedy search method (CAS + G), which focuses on finding the highest rated network structure, and a local learning method (CAS + L), which focuses  ...  on faster learning the structure with little loss of quality.  ...  Acknowledgements We thank the members of the Natural Computing group for thoughtful discussions.  ... 
doi:10.1186/s12864-017-4228-y pmid:29219084 pmcid:PMC5773867 fatcat:xbxticzk2jd6tbvm5e35zzrvqq

The max-min hill-climbing Bayesian network structure learning algorithm

Ioannis Tsamardinos, Laura E. Brown, Constantin F. Aliferis
2006 Machine Learning  
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC).  ...  It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges.  ...  Finally, we would like to thank the editor and the anonymous reviewers for their helpful feedback and for providing a draft of the computational complexity comparison between the Greedy Search and MMHC  ... 
doi:10.1007/s10994-006-6889-7 fatcat:gvnojdmug5andeawld3e6xkvjy

Inferring Transcriptional Regulatory Relationships Among Genes in Breast Cancer: An Application of Bayes' Theorem

Emmanuel S. Adabor, George K. Acquaah-Mensah, Francis T. Oduro
2014 International Journal of Statistics and Probability  
This subset was supplied to a Bayesian Network inference learning algorithm to unearth new regulatory relationships from the data.  ...  Gene networks may be inferred from such microarray data.  ...  Inferring Networks From Data The process of learning the Bayesian structure from the breast cancer data is shown in Figure 2 .  ... 
doi:10.5539/ijsp.v3n2p52 fatcat:5nqktk4i45ggvawo5d3kvfwwq4

Bayesian Network Structure Learning Approach Based on Searching Local Structure of Strongly Connected Components

Kunhua Zhong, Yuwen Chen, Ju Zhang, Xiaolin Qin
2022 IEEE Access  
Learning the structure of Bayesian networks is a challenging problem because it is a NP-Hard problem.  ...  INDEX TERMS Bayesian network, structure learning, hill climbing search, strongly connected component.  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.  ... 
doi:10.1109/access.2022.3178842 fatcat:5gndwpi7anckvoh4lh7mipgk6u

Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets [article]

Mauro Scanagatta, Giorgio Corani, Marco Zaffalon, Jaemin Yoo, U Kang
2018 arXiv   pre-print
Learning a Bayesian networks with bounded treewidth is important for reducing the complexity of the inferences.  ...  We present a novel anytime algorithm (k-MAX) method for this task, which scales up to thousands of variables.  ...  We thank Cassio de Polpo Campos for critical discussions on the topics of this paper.  ... 
arXiv:1802.02468v1 fatcat:aixppnzv25a6vgvwcmjtzqdpd4

MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals

Dana Pe'er, Amos Tanay, Aviv Regev
2006 Journal of machine learning research  
Here we define a constrained family of Bayesian network structures suitable for this domain and devise an efficient search algorithm that utilizes these structural constraints to find high scoring networks  ...  Since the gene expression domain involves a large number of variables and a limited number of samples, it poses both computational and statistical challenges to Bayesian network learning algorithms.  ...  Our novel greedy algorithm for this task, MinReg (sketched in Figure 4 ), begins with an empty set of regulators and an empty graph structure.  ... 
dblp:journals/jmlr/PeerTR06 fatcat:sngtmafjjbgsjg6h7jxrgsjee4

Critiquing Knowledge Representation in Medical Image Interpretation Using Structure Learning [chapter]

Niels Radstake, Peter J. F. Lucas, Marina Velikova, Maurice Samulski
2011 Lecture Notes in Computer Science  
We subsequently carried out extensive experiments with Bayesian-network structure learning, for critiquing the Bayesian network.  ...  For this paper, we investigated the use of Bayesian networks as a knowledge-representation formalism, where the structure was drafted by hand and the probabilistic parameters learnt from image data.  ...  For the greedy search algorithm an empty network was used as an initial structure. Results Learning structures based on an expert model.  ... 
doi:10.1007/978-3-642-18050-7_5 fatcat:6jl5exehczeldhvps2omgnnmte

A SMILE web-based interface for learning the causal structure and performing a diagnosis of a Bayesian network

Nipat Jongsawat, Wichian Premchaiswadi
2009 2009 IEEE International Conference on Systems, Man and Cybernetics  
Learning the structure of a Bayesian network model and causal relations from a dataset or database is important for large BNs analysis.  ...  This paper focuses on using a SMILE web-based interface for building the structure of BN models from a dataset by using different structural learning algorithms.  ...  ACKNOWLEDGMENT The authors would like to thank the Decision Systems Laboratory, University of Pittsburgh for supporting documents, and source file of the engines: Structural Modeling, Inference, and Learning  ... 
doi:10.1109/icsmc.2009.5346198 dblp:conf/smc/JongsawatP09a fatcat:q5ncofq5n5crlgajgmh734wrhi

Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering [article]

Yuhong Guo, Dale Schuurmans
2012 arXiv   pre-print
We present a new approach to learning the structure and parameters of a Bayesian network based on regularized estimation in an exponential family representation.  ...  a valid network structure.  ...  Conclusion We have presented what we feel is a promising new perspective on learning Bayesian network structure from data.  ... 
arXiv:1206.6832v1 fatcat:ro5qobyavvgivcuknxgrqvtl24
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