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Restricted Bayesian Network Structure Learning [chapter]

Peter J. F. Lucas
2004 Studies in Fuzziness and Soft Computing  
As a consequence, researchers have studied learning Bayesian networks with a fixed structure, notably naive Bayesian networks and tree-augmented Bayesian networks, which involves no search at all.  ...  Learning the structure of a Bayesian network from data is a difficult problem, as its associated search space is superexponentially large.  ...  The FAN algorithm is an example of a restricted Bayesian-network structure learning algorithm [4] .  ... 
doi:10.1007/978-3-540-39879-0_12 fatcat:gwt7tdyc5fawdpt3l2qyn2hbme

Learning restricted Bayesian network structures [article]

Raymond Hemmecke, Silvia Lindner, Milan Studený
2010 arXiv   pre-print
In this paper we deal with the complexity of learning restricted Bayesian network structures, that is, we wish to find network structures of highest score within a given subset of all possible network  ...  restricted Bayes network structures.  ...  Learning restricted Bayesian network structures A lot of research is devoted to the topic of finding complexity results of the general problem of learning Bayesian network structures analyzing different  ... 
arXiv:1011.6664v1 fatcat:sfdgoharzrgvjbyqgfmxvsx4wm

Bayesian network learning algorithms using structural restrictions

Luis M. de Campos, Javier G. Castellano
2007 International Journal of Approximate Reasoning  
The use of several types of structural restrictions within algorithms for learning Bayesian networks is considered.  ...  The main goal of this paper is to study whether the algorithms for automatically learning the structure of a Bayesian network from data can obtain better results by using this prior knowledge.  ...  learning Bayesian networks.  ... 
doi:10.1016/j.ijar.2006.06.009 fatcat:fnlxh4okxzfqzidnuebwjtdmn4

An Algorithm for Bayesian Networks Structure Learning Based on Simulated Annealing with MDL Restriction

Shuisheng Ye, Hong Cai, Rongguan Sun
2008 2008 Fourth International Conference on Natural Computation  
Basically, Bayesian Belief Networks (BBNs) as probabilistic tools provide suitable facilities for modelling process under uncertainty.  ...  Finding the beststructure (structure learning) ofthe DAG is a classic NP-Hard problem in BBNs.  ...  Bayesian Belief Networks: Bayesian belief networks or BBN is named based on studies of Thomas Bayes (1702-and the parameter learning is a method for learning in 1761) in the field of probability theory  ... 
doi:10.1109/icnc.2008.658 dblp:conf/icnc/YeCS08 fatcat:72nhkekqmzhyhgn27djeegc7zy

Enabling Large-Scale Bayesian Network Learning by Preserving Intercluster Directionality

2007 IEICE transactions on information and systems  
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 scoring-based learning of a Bayesian network B comprises two parts; learning a DAG structure G and learning probabilistic parameters P.  ... 
doi:10.1093/ietisy/e90-d.7.1018 fatcat:naeetmbelnexjfwtaybipfmknq

H-CORE: Enabling genome-scale Bayesian analysis of biological systems without prior knowledge

Sungwon Jung, Kwang H. Lee, Doheon Lee
2007 Biosystems (Amsterdam. Print)  
However, the scalability of those approaches is seriously restricted because of the huge search space for finding an optimal DAG structure in the process of Bayesian network learning.  ...  In this paper, we use the hierarchical clustering and order restriction (H-CORE) method for the learning of large Bayesian networks by clustering entities and restricting edge directions between those  ...  Bayesian network learning Conventional Bayesian network learning The learning for a Bayesian network B = G, P using given observed data D involves two steps: learning the graph structure G and learning  ... 
doi:10.1016/j.biosystems.2006.08.004 pmid:17005318 fatcat:cc77pxgeqjaqdfluvj564s2zae

BNFinder: exact and efficient method for learning Bayesian networks

B. Wilczynski, N. Dojer
2008 Bioinformatics  
It supports dynamic Bayesian networks and, if the variables are partially ordered, also static Bayesian networks.  ...  Results: We present a BNFinder software, which allows for Bayesian network reconstruction from experimental data.  ...  ., 2004; Meek, 2001) showing that, without restrictive assumptions, learning Bayesian networks from data is NP-hard with respect to the number of network vertices.  ... 
doi:10.1093/bioinformatics/btn505 pmid:18826957 pmcid:PMC2639006 fatcat:zxa4gmzgznbvff44dmlwnqvjyq

Learning Bayesian network structure based on the classification and regression tree

Yan Sun, Yi-Yuan Tang
2008 BMC Neuroscience  
a novel structure learning method to construct the Bayesian network from these data, and the method need not prescribe a previous sequence.  ...  In order to overcome limitations of the ordinary algorithm that imposes a previous ordering on the domain attributes that restricts the number of Bayesian structures to be learned, in this paper, we present  ...  a novel structure learning method to construct the Bayesian network from these data, and the method need not prescribe a previous sequence.  ... 
doi:10.1186/1471-2202-9-s1-p71 fatcat:wjccvkbbyrhubhqfrullborgqa

A theory of inferred causation [chapter]

Judea Pearl, Thomas S. Verma
1995 Studies in Logic and the Foundations of Mathematics  
models: Dynamic Bayesian Networks (DBNs) Continuing Research on Continuing Research on Learning Bayesian Networks from Data Learning Bayesian Networks from Data Kansas State University Department of Computing  ...  Kansas State University Department of Computing and Information Sciences CIS 830: Advanced Topics in Artificial Intelligence Learning Bayesian Networks: Learning Bayesian Networks: Missing Observations  ... 
doi:10.1016/s0049-237x(06)80074-1 fatcat:gihsurzxdjebhkbtilxcjlhtqi

On the Use of Restrictions for Learning Bayesian Networks [chapter]

Luis M. de Campos, Javier G. Castellano
2005 Lecture Notes in Computer Science  
In this paper we explore the use of several types of structural restrictions within algorithms for learning Bayesian networks.  ...  Bayesian network learning algorithms based on the score+search paradigm.  ...  The paper is structured as follows: in Section 2 we briefly give some preliminary basic concepts about learning the structure of Bayesian networks.  ... 
doi:10.1007/11518655_16 fatcat:bstsaijs55hyvbcvb4ekbl5cpu

Construction of Large-Scale Bayesian Networks by Local to Global Search [chapter]

Kyu-Baek Hwang, Jae Won Lee, Seung-Woo Chung, Byoung-Tak Zhang
2002 Lecture Notes in Computer Science  
Most existing algorithms for structural learning of Bayesian networks are suitable for constructing small-sized networks which consist of several tens of nodes.  ...  The proposed method has been evaluated on two benchmark datasets and a real-life DNA microarray data, demonstrating the ability to learn the large-scale Bayesian network structure efficiently.  ...  Hence, the task is to learn the structure of Bayesian network with 890 nodes.  ... 
doi:10.1007/3-540-45683-x_41 fatcat:r672euhaungaldveuwmeltm6s4

Fuzzy Naive Bayesian for constructing regulated network with weights

Xi Y. Zhou, Xue W. Tian, Joon S. Lim, Feng Liu, Dong-Hoon Lee, Ricardo Lagoa, Sandeep Kumar
2015 Bio-medical materials and engineering  
Finally, we will classify the heart and Haberman datasets by the FNB network to compare with experiments of Naive Bayesian and TAN.  ...  Therefore, this paper proposes a new algorithm named Fuzzy Naive Bayesian (FNB) using neural network with weighted membership function (NEWFM) to extract regulated relations and weights.  ...  However, learning Bayesian network without any restrictions is very time consuming.  ... 
doi:10.3233/bme-151476 pmid:26405944 fatcat:rzppazgwu5f5ljawnlb4pnyh7u


2000 Applied Physics Letters  
This paper discusses in depth the role that the inclusion order plays in learning the structure of Bayesian networks.  ...  Two or more Bayesian network structures are Markov equivalent when the corresponding acyclic digraphs encode the same set of conditional independencies.  ...  In the context of structure learning, the Bayesian network structure is often identified as the Bayesian network itself because learning the parameters can be done once the structure has been learned.  ... 
doi:10.1162/153244304773936045 fatcat:vvsgnjtn5jbppjp36wmya3kkna

An Improved De-noising Algorithm for Bayesian Network Classifiers Parameter Learning

Qing KANG, Li-Qing WANG, Yong-Yue XU, Hong LI, Hong-Ping AN, Xing-Chao WANG, Han-Bing YAO
2017 DEStech Transactions on Engineering and Technology Research  
Restricted Bayesian network learning usually consists of two main steps [8, 9] : structure learning and parameters learning. This paper focuses on the study of parameters learning.  ...  Based on the Bayesian network parameters learning, this paper redesigns the confidence measure function, and then put forward a kind of noise reduction algorithm which is suitable for the limited structure  ...  [12, 13] proposed a cluster based approach for parameters learning in restricted Bayesian networks.  ... 
doi:10.12783/dtetr/sste2016/6495 fatcat:rhygmxzn7fd2tlfywls3gzcwjy

A Bayesian Network Learning Algorithm Based on Independence Test and Ant Colony Optimization

Jun-Zhong JI, Hong-Xun ZHANG, Ren-Bing HU, Chun-Nian LIU
2009 Acta Automatica Sinica  
To solve the drawbacks of the ant colony optimization for learning Bayesian networks (ACO-B), this paper proposes an improved algorithm based on the conditional independence test and ant colony optimization  ...  First, the I-ACO-B uses order-0 independence tests to effectively restrict the space of candidate solutions, so that many unnecessary searches of ants can be avoided.  ...  To solve the drawbacks of the ant colony optimization for learning Bayesian networks [6] (ACO-B), this paper proposes a Bayesian network structure learning algorithm based on the conditional independence  ... 
doi:10.1016/s1874-1029(08)60077-4 fatcat:xfdidmbr4rg7lloh6wcjrlnh3q
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