A Construction of Bayesian Networks from Databases Based on an MDL Principle

Suzuki, Joe

doi:10.1016/b978-1-4832-1451-1.50037-8

Cited by 120 publications

(75 citation statements)

References 18 publications

(38 reference statements)

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“…The LL score is better, in general, for complete networks, and for this reason alternate scoring functions have been proposed that penalize the LL according to some factor. The Minimum Description Length [34] (MDL), the Akaike Information Criterion [1] (AIC), and Bayesian Information Criterion [30] (BIC) (roughly…”

Section: Learning and Scoring Bayesian Networkmentioning

confidence: 99%

Widened Learning of Bayesian Network Classifiers

Sampson

Berthold

2016

Lecture Notes in Computer Science

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Abstract. We demonstrate the application of Widening to learning performant Bayesian Networks for use as classifiers. Widening is a framework for utilizing parallel resources and diversity to find models in a hypothesis space that are potentially better than those of a standard greedy algorithm. This work demonstrates that widened learning of Bayesian Networks, using the Frobenius Norm of the networks' graph Laplacian matrices as a distance measure, can create Bayesian networks that are better classifiers than those generated by popular Bayesian Network algorithms.

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Section: Learning and Scoring Bayesian Networkmentioning

confidence: 99%

Widened Learning of Bayesian Network Classifiers

Sampson

Berthold

2016

Lecture Notes in Computer Science

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“…Similarity was defined as the ratio of the number of incidences of joint selection of dependency as determined by knowledge data and information criterion to the total number of two-node sets. Specifically, using similarity, we chose the Akaike Information Criterion (AIC) [21] for the representative node search and Minimum Description Length (MDL) [22] for the whole model construction.…”

Section: Constructed Bayesian Network Modelmentioning

confidence: 99%

User-Adapted Recommendation of Content on Mobile Devices Using Bayesian Networks

Iwasaki

Mizuno

Hara

et al. 2010

IEICE Trans. Inf. & Syst.

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SUMMARYMobile devices, such as cellular phones and car navigation systems, are essential to daily life. People acquire necessary information and preferred content over communication networks anywhere, anytime. However, usability issues arise from the simplicity of user interfaces themselves. Thus, a recommendation of content that is adapted to a user's preference and situation will help the user select content. In this paper, we describe a method to realize such a system using Bayesian networks. This user-adapted mobile system is based on a user model that provides recommendation of content (i.e., restaurants, shops, and music that are suitable to the user and situation) and that learns incrementally based on accumulated usage history data. However, sufficient samples are not always guaranteed, since a user model would require combined dependency among users, situations, and contents. Therefore, we propose the LK method for modeling, which complements incomplete and insufficient samples using knowledge data, and CPT incremental learning for adaptation based on a small number of samples. In order to evaluate the methods proposed, we applied them to restaurant recommendations made on car navigation systems. The evaluation results confirmed that our model based on the LK method can be expected to provide better generalization performance than that of the conventional method. Furthermore, our system would require much less operation than current car navigation systems from the beginning of use. Our evaluation results also indicate that learning a user's individual preference through CPT incremental learning would be beneficial to many users, even with only a few samples. As a result, we have developed the technology of a system that becomes more adapted to a user the more it is used.

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“…Several scoring functions have been proposed in the literature [4,10,12,15]. The discussion of the advantages and disadvantages of each of these functions is outside the scope of this paper.…”

Section: Bayesian Networkmentioning

confidence: 99%

Learning bayesian networks consistent with the optimal branching

Yang

Létourneau

2007

Sixth International Conference on Machine Learning and Applications (ICMLA 2007)

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We introduce a polynomial-time algorithm to learn Bayesian networks whose structure is restricted to nodes with in-degree at most k and to edges consistent with the optimal branching, that we call consistent k-graphs (CkG). The optimal branching is used as an heuristic for a primary causality order between network variables, which is subsequently refined, according to a certain score, into an optimal CkG Bayesian network. This approach augments the search space exponentially, in the number of nodes, relatively to trees, yet keeping a polynomial-time bound. The proposed algorithm can be applied to scores that decompose over the network structure, such as the well known LL, MDL, AIC, BIC, K2, BD, BDe, BDeu and MIT scores. We tested the proposed algorithm in a classification task. We show that the induced classifier always score better than or the same as the Naive Bayes and Tree Augmented Naive Bayes classifiers. Experiments on the UCI repository show that, in many cases, the improved scores translate into increased classification accuracy.

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A Construction of Bayesian Networks from Databases Based on an MDL Principle

Cited by 120 publications

References 18 publications

Widened Learning of Bayesian Network Classifiers

Widened Learning of Bayesian Network Classifiers

User-Adapted Recommendation of Content on Mobile Devices Using Bayesian Networks

Learning bayesian networks consistent with the optimal branching

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