The Search of Causal Orderings: A Short Cut for Learning Belief Networks

Acid, Silvia; Campos, Luis M. de; Huete, Juan F.

doi:10.1007/3-540-44652-4_20

Cited by 6 publications

(4 citation statements)

References 16 publications

(29 reference statements)

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“…There has been much confusion over when utilizing causal networks is applicable (Druzdzel & Simon, 1993). Many studies (probably wrongly) have assumed that Bayesian networks and causal Bayesian networks are equivalent (Acid & de Campos, 1995; Acid et al ., 2001); more careful studies set out their assumptions clearly beforehand. Most of the work in learning causal networks has focused on constraint-based algorithms, building on work from Glymour et al .…”

Section: Learning Bayesian Network Structuresmentioning

confidence: 99%

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Learning Bayesian networks: approaches and issues

Daly

Shen

Aitken

2011

The Knowledge Engineering Review

269

194

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Daly, R., Shen, Q., Aitken, S. (2011). Learning Bayesian networks: approaches and issues. Knowledge Engineering Review, 26 (2), 99-157Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to the difficulty domain experts have in specifying them, techniques that learn Bayesian networks from data have become indispensable. Recently, however, there have been many important new developments in this field. This work takes a broad look at the literature on learning Bayesian networks?in particular their structure?from data. Specific topics are not focused on in detail, but it is hoped that all the major fields in the area are covered. This article is not intended to be a tutorial?for this, there are many books on the topic, which will be presented. However, an effort has been made to locate all the relevant publications, so that this paper can be used as a ready reference to find the works on particular sub-topics.Peer reviewe

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Section: Learning Bayesian Network Structuresmentioning

confidence: 99%

“…Acid et al . (2001) use an approach similar to Singh and Valtorta (1995) as seen in Section 4.9.1, by using CI tests. However, instead of learning a definite ordering, a search is performed to preserve as many of the CIs as possible.…”

Section: Learning Bayesian Network Structuresmentioning

confidence: 99%

Learning Bayesian networks: approaches and issues

Daly

Shen

Aitken

2011

The Knowledge Engineering Review

269

194

View full text Add to dashboard Cite

show abstract

“…More empirical studies are also required to definitively establish the validity of our approach. On the other hand, several authors [3,16,19,24,33] have successfully used the variable ordering space for learning BNs. It would also be interesting, therefore, to apply ACO to search in the space of orderings.…”

Section: Discussionmentioning

confidence: 99%

Ant colony optimization for learning Bayesian networks

Campos

Fernández‐Luna

Gámez

et al. 2002

International Journal of Approximate Reasoning

Self Cite

159

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One important approach to learning Bayesian networks (BNs) from data uses a scoring metric to evaluate the fitness of any given candidate network for the data base, and applies a search procedure to explore the set of candidate networks. The most usual search methods are greedy hill climbing, either deterministic or stochastic, although other techniques have also been used. In this paper we propose a new algorithm for learning BNs based on a recently introduced metaheuristic, which has been successfully applied to solve a variety of combinatorial optimization problems: ant colony optimization (ACO). We describe all the elements necessary to tackle our learning problem using this metaheuristic, and experimentally compare the performance of our ACObased algorithm with other algorithms used in the literature. The experimental work is carried out using three different domains: ALARM, INSURANCE and BOBLO.

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“…With this restriction, the space cardinality holding all the structures is determined by 2   n 2 , where n is the number of nodes in the graph (Acid and Huete, 2001). …”

Section: Bayesian Network Approachmentioning

confidence: 99%

Bayesian Network Modeling: A Case Study of Credit Scoring Analysis of Consumer Loans Default Payment

Abid

Zaghdene

Masmoudi

et al. 2017

Asian Economic and Financial Review

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Article History JEL Classification C1, C11.This paper deals with the issue of predicting customers' default payment. The Bayesian network credit model is applied for the prediction and classification of personal loans with regard to credit worthiness. Referring to credit experts and using K2 algorithm for learning structure, we set up the dependency conditional relations between variables that explain default payments. Then, the parametric learning is adopted to detect conditional probabilities of customers' default payment. The parameters are estimated on the basis of real personal loan data obtained from a Tunisian commercial bank. The Bayesian network analysis has revealed that customers' age, gender, type of credit, professional status, and monthly repayment burden and credit duration have an important predictive power for the detection of customers' default payment. Therefore, our findings allow providing an effective decision support system for banks in order to detect and reduce the rate of bad borrowers through the use of a Bayesian Network model.

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The Search of Causal Orderings: A Short Cut for Learning Belief Networks

Cited by 6 publications

References 16 publications

Learning Bayesian networks: approaches and issues

Learning Bayesian networks: approaches and issues

Ant colony optimization for learning Bayesian networks

Bayesian Network Modeling: A Case Study of Credit Scoring Analysis of Consumer Loans Default Payment

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