Bayesian Network Structure Ensemble Learning

Liu, Feng; Tian, Fengzhan; Zhu, Qiliang

doi:10.1007/978-3-540-73871-8_42

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…In high dimensional features spaces, Bagging algorithm is not appropriate and impractical for Bayesian Networks and its complexity may overestimate extra edges and their distribution might not satisfy Markov Blanket condition and faithfulness condition [13], [14]. Therefore, this chapter proposed to solve this problem by reducing dimensionality before bagging while preserving efficiency and accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, in causal discovery, there are some disadvantages for BNs learning using Bagging. Bootstrap method can add many extra edges in graphical model due to more complexity especially in high dimensional features with limited dataset [13]. Moreover, distribution from bootstrap dataset may not satisfy Markov Blanket condition and faithfulness condition [14].…”

Section: Ensemble Classifiermentioning

confidence: 99%

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Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers

Duangsoithong

Windeatt

2011

Ensembles in Machine Learning Applications

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PC and TPDA algorithms are robust and well known prototype algorithms, incorporating constraint-based approaches for causal discovery. However, both algorithms cannot scale up to deal with high dimensional data, that is more than few hundred features. This chapter presents hybrid correlation and causal feature selection for ensemble classifiers to deal with this problem. Redundant features are removed by correlation-based feature selection and then irrelevant features are eliminated by causal feature selection. The number of eliminated features, accuracy, the area under the receiver operating characteristic curve (AUC) and false negative rate (FNR) of proposed algorithms are compared with correlation-based feature selection (FCBF and CFS) and causal based feature selection algorithms (PC, TPDA, GS, IAMB).

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Section: Discussionmentioning

confidence: 99%

Section: Ensemble Classifiermentioning

confidence: 99%

Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers

Duangsoithong

Windeatt

2011

Ensembles in Machine Learning Applications

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show abstract

“…Equation 4 seeks to find the network with the greatest BDeu score. On the other side, moving the solution about in some medium affects it, in a mild manner (Liu F, Tian F, Zhu Q. (2007Liu F, Tian F, Zhu Q.…”

Section: Pigeon Inspired Optimizationmentioning

confidence: 99%

“…On the other side, moving the solution about in some medium affects it, in a mild manner (Liu F, Tian F, Zhu Q. (2007Liu F, Tian F, Zhu Q. (2007.…”

Section: Pigeon Inspired Optimizationmentioning

confidence: 99%

Structure Learning of Bayesian Network Using Swarm Intelligent Algorithm A Review

2022

QZJ

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Machines using Bayesian networks can be used to construct the framework of information in artificial intelligence that connects the variables in a probabilistic way. "Deleting, reversing, moving, and inserting" is an approach to finding the best answer to the proposition of the problem in the algorithm. 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

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“…It is an uncomplicated setup in this particular domain and only replaces one competitor edge per time, with the four operators working around the board. On the other side, moving the solution about in some medium affects it, in a mild manner [23]. Correcting a solution without the three basic operators would not automatically result in a better solution [21].…”

Section: 𝑉𝑖(𝑡) = 𝑉𝑖(𝑡 − 1) 𝑒 −𝑅𝑡 + 𝑟𝑎𝑛𝑑 (𝑃𝑔 − 𝑃𝑖(𝑡 − 1))mentioning

confidence: 99%

Structure learning of bayesian network using swarm intelligent algorithm: a review

Kareem,

Askar,

Ahmed

2022

Bull.Socinf.The.App

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Machines using Bayesian networks can be used to construct the framework of information in artificial intelligence that connects the variables in a probabilistic way. “Deleting, reversing, moving, and inserting” is an approach to finding the best answer to the proposition of problem in the algorithm. In the Enhanced Surface Water Searching Technique, mostly, the hunt for water is done by elephants during dry seasons, It is Pigeon Optimization, Simulated Annealing, Greedy search, and the BDeu metrics being reviewed in combination to evaluate all these strategies being used in order to solve this problem. They subjected different data sets to the uncertainty matrix in an investigation to find out which of these approaches performed best. According to evaluation data, the algorithm shows stronger results and delivers better points. Additionally, this article also represents the structure learning processes for Bayesian Network as well.

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Bayesian Network Structure Ensemble Learning

Cited by 9 publications

References 5 publications

Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers

Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers

Structure Learning of Bayesian Network Using Swarm Intelligent Algorithm A Review

Structure learning of bayesian network using swarm intelligent algorithm: a review

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