A Bayesian Network Based Approach for Data Classification Using Structural Learning

2008 ◽  
pp. 25-32 ◽  
Author(s):  
A. R. Khanteymoori ◽  
M. M. Homayounpour ◽  
M. B. Menhaj
Keyword(s):  
Bayesian Network ◽  
Data Classification ◽  
Structural Learning

A Bayesian network structural learning algorithm for calculating the failure probabilities of complex engineering systems with limited data

2021 ◽  
pp. 1-14
Author(s):  
Yong Chen ◽  
Tianbao Zhang ◽  
Ruojun Wang ◽  
Lei Cai
Keyword(s):  
Bayesian Network ◽  
Learning Algorithm ◽  
Hill Climbing ◽  
Structural Learning ◽  
Engineering Systems ◽  
Limited Data ◽  
Model Complex ◽  
Complex Engineering ◽  
Complex Engineering Systems ◽  
Three Components

The failure of complex engineering systems is easy to lead to disastrous consequences. To prevent the failure, it is necessary to model complex engineering systems using probabilistic techniques with limited data which is a major feature of complex engineering systems. It is a good choice to perform such modeling using Bayesian network because of its advantages in probabilistic modeling. However, few Bayesian network structural learning algorithms are designed for complex engineering systems with limited data. Therefore, an algorithm for learning the Bayesian network structure of them should be developed. Based on the process of self-purification of water, a complex engineering system is segmented into three components according to the degree of difficulty in solving them. And then a Bayesian network learning algorithm with three components (TC), including PC algorithm, MIK algorithm which is originated by the paper through combining Mutual Information and K2 algorithm, and the Hill-Climbing method, is developed, i.e. TC algorithm. To verify its effectiveness, TC algorithm, K2 algorithm, and Max-Min Hill-Climbing are respectively used to learn Alarm network with different sizes of samples. The results imply that TC algorithm has the best performance. Finally, TC algorithm is applied to study tank spill accidents with 220 samples.

Learning Bayesian Network Structure Using a MultiExpert Approach

2014 ◽  
Vol 24 (02) ◽  
pp. 269-284 ◽  
Author(s):  
Francesco Colace ◽  
Massimo De Santo ◽  
Luca Greco
Keyword(s):  
Bayesian Network ◽  
Network Structure ◽  
Scientific Community ◽  
Text Categorization ◽  
Experimental Validation ◽  
Majority Vote ◽  
Learning Algorithms ◽  
Structural Learning ◽  
Bayesian Network Structure ◽  
Ontology Building

The learning of a Bayesian network structure, especially in the case of wide domains, can be a complex, time-consuming and imprecise process. Therefore, the interest of the scientific community in learning Bayesian network structure from data is increasing: many techniques or disciplines such as data mining, text categorization, and ontology building, can take advantage from this process. In the literature, there are many structural learning algorithms but none of them provides good results for each dataset. This paper introduces a method for structural learning of Bayesian networks based on a MultiExpert approach. The proposed method combines five structural learning algorithms according to a majority vote combining rule for maximizing their effectiveness and, more generally, the results obtained by using of a single algorithm. This paper shows an experimental validation of the proposed algorithm on standard datasets.

scholarly journals Learning Bayesian Networks: The Combination of Scoring Function and Dataset

2020 ◽  
Vol 9 (5) ◽  
pp. 149-154
Keyword(s):  
Tabu Search ◽  
Bayesian Network ◽  
Graphical Model ◽  
Scoring Function ◽  
Random Variables ◽  
Structural Learning ◽  
Parameter Learning ◽  
Main Study ◽  
Relationship Of ◽  
Made In

Bayesian network (BN), a graphical model consists nodes and directed edges, which representing random variables and relationship of the corresponding random variables, respectively. The main study of Bayesian network is structural learning and parameter learning. There are score-and-search based, constraint based and hybrid based in forming the network structure. However, there are many types of scores and algorithms available in the structural learning of Bayesian network. Hence, the objective of this study is to determine the best combination of scores and algorithms for various types of datasets. Besides, the convergence of time in forming the BN structure with datasets of different sizes has been examined. Lastly, a comparison between score-and-search based and constraint based methods is made in this study. At the end of this study, it has been observed that Tabu search has the best combination with the scoring function regardless of the size of dataset. Furthermore, it has been found that when the dataset is large, the time it takes for a BN structure to converge is shorter. Last but not least, results showed that the score-and-search based algorithm performs better as compared to constraint based algorithm.

scholarly journals Discovering Causal Relations in Semantically-Annotated Probabilistic Business Process Diagrams

10.29007/nd7r ◽  
2018 ◽  
Author(s):  
Hector Ceballos ◽  
Francisco Cantu
Keyword(s):  
Bayesian Network ◽  
Business Process ◽  
Process Modeling ◽  
Business Processes ◽  
Process Development ◽  
Structural Learning ◽  
Causal Relations ◽  
Graphical Notation ◽  
On Line ◽  
Process Diagrams

Business Process Diagrams (BPDs) have been used for documenting, analyzing and optimizing business processes. Business Process Modeling and Notation (BPMN) provides a rich graphical notation which is supported by a formalization that permits users automating such tasks. Stochastic versions of BPMN allows designers to represent the probability every possible way a process can develop. Nevertheless, this support is not enough for representing conditional dependencies between events occurring during process development. We show how structural learning on a Bayesian Network obtained from a BPD is used for discovering causal relations between process events. Temporal precedence between events, captured in the BPD, is used for pruning and correcting the model discovered by an Inferred Causation (IC) algorithm. We illustrate our approach by detecting dishonest bidders in an on-line auction scenario.

scholarly journals A New Ensemble Learning Algorithm Combined with Causal Analysis for Bayesian Network Structural Learning

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2054
Author(s):  
Ming Li ◽  
Ren Zhang ◽  
Kefeng Liu
Keyword(s):  
Bayesian Network ◽  
Ensemble Learning ◽  
Causal Reasoning ◽  
Learning Algorithm ◽  
Training Sample ◽  
Causal Analysis ◽  
Local Optimum ◽  
Structural Learning ◽  
Ensemble Learning Algorithm ◽  
Bagging Method

The Bayesian Network (BN) has been widely applied to causal reasoning in artificial intelligence, and the Search-Score (SS) method has become a mainstream approach to mine causal relationships for establishing BN structure. Aiming at the problems of local optimum and low generalization in existing SS algorithms, we introduce the Ensemble Learning (EL) and causal analysis to propose a new BN structural learning algorithm named C-EL. Combined with the Bagging method and causal Information Flow theory, the EL mechanism for BN structural learning is established. Base learners of EL are trained by using various SS algorithms. Then, a new causality-based weighted ensemble way is proposed to achieve the fusion of different BN structures. To verify the validity and feasibility of C-EL, we compare it with six different SS algorithms. The experiment results show that C-EL has high accuracy and a strong generalization ability. More importantly, it is capable of learning more accurate structures under the small training sample condition.

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