Entropy-based pruning for learning Bayesian networks using BIC

Campos, Cassio P. de; Scanagatta, Mauro; Corani, Giorgio; Zaffalon, Marco

doi:10.1016/j.artint.2018.04.002

Cited by 32 publications

(23 citation statements)

References 6 publications

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“…To infer or evaluate borrowers' credit based on BN, we first need to learn the BN model ( , ) from data set , including its structure and parameters. Generally, the BIC score [8] can be used to measure the model; the higher the BIC score, the better the obtained BN model.…”

Section: Bn Modelmentioning

confidence: 99%

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Learning P2P Lending Credit Evaluation Bayesian Network from Missing Data

Lv¹

2019

IJPE

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Credit evaluation is an important issue for investors in the financial field. However, there is a large amount of missing data in the P2P lending platform. To evaluate borrowers' credit from missing data, a credit evaluation Bayesian network model learning algorithm is proposed based on domain knowledge. Specifically, we first give a credit evaluation Bayesian network (CEBN) model to represent the borrowers' attributions and the relationships between attributions, and then we design the CEBN learning algorithm based on domain knowledge. Furthermore, we analyze and discuss the time complexity of the algorithm. Finally, the experimental results demonstrate that the CEBN model has good interpretability, learning performance, and evaluation performance by comparing it with other methods.

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Section: Bn Modelmentioning

confidence: 99%

“…The Bayesian network (BN) [6] can be used to represent and deal with uncertainty relationships between variables and has good interpretability, so many efficient BN model learning algorithms [7][8][9][10] have been proposed. The BN model has been applied in many fields.…”

Section: Introductionmentioning

confidence: 99%

Learning P2P Lending Credit Evaluation Bayesian Network from Missing Data

Lv¹

2019

IJPE

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“…(1) The criteria of model selection are also called the scoring function, and the function evaluates the fitting degree between the learned model and the observed data . Here, we only introduce the BIC (Bayesian information criterion) scoring function [12][13], and its form is…”

Section: Bayesian Network Modelmentioning

confidence: 99%

Bayesian Network Model for Learning Arithmetic Concepts

Lv¹,

Tong²,

Qian³

et al. 2019

Int J Performability Eng

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An object usually belongs to multiple concepts, but some concepts can be judged directly while other concepts need to be inferred indirectly. To learn some arithmetic concepts from positive integer number sets, we address an arithmetic concept Bayesian network (ACBN) model by taking advantage of Bayesian networks. Specifically, we first give an ACBN model to represent the arithmetic concept knowledge and their direct relationships, and then we design an ACBN model learning algorithm based on domain knowledge. Furthermore, to infer indirectly some arithmetic concepts, we design the learning method of evidence concepts based on the idea of knearest neighbors, and then we propose the inference algorithm of the ACBN model. Finally, the experimental results demonstrate that the ACBN model can effectively learn some daily arithmetic concepts.

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“…Wang et al [23] considered incorporating ancestral constraints into exact learning algorithms based on the order graph. Tan et al [24] proposed a bidirectional heuristic algorithm to search the order graph. The above algorithms are more based on search strategies to improve the efficiency of the exact learning of the BN structure.…”

Section: Introductionmentioning

confidence: 99%

Causal constraint pruning for exact learning of Bayesian network structure

Tan

Gao

et al. 2021

J. of Syst. Eng. Electron.

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How to improve the efficiency of exact learning of the Bayesian network structure is a challenging issue. In this paper, four different causal constraints algorithms are added into score calculations to prune possible parent sets, improving state-ofthe-art learning algorithms' efficiency. Experimental results indicate that exact learning algorithms can significantly improve the efficiency with only a slight loss of accuracy. Under causal constraints, these exact learning algorithms can prune about 70% possible parent sets and reduce about 60% running time while only losing no more than 2% accuracy on average. Additionally, with sufficient samples, exact learning algorithms with causal constraints can also obtain the optimal network. In general, adding max-min parents and children constraints has better results in terms of efficiency and accuracy among these four causal constraints algorithms.

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Entropy-based pruning for learning Bayesian networks using BIC

Cited by 32 publications

References 6 publications

Learning P2P Lending Credit Evaluation Bayesian Network from Missing Data

Learning P2P Lending Credit Evaluation Bayesian Network from Missing Data

Bayesian Network Model for Learning Arithmetic Concepts

Causal constraint pruning for exact learning of Bayesian network structure

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