An Order-based Algorithm for Learning Structure of Bayesian Networks

Shahab Behjati, Hamid Beigy
; Proceedings of the Ninth International Conference on Probabilistic Graphical Models, PMLR 72:25-36, 2018.

Abstract

In this paper, we study the problem learning structure of Bayesian networks from data. The problem of Bayesian networks structure learning (BNSL) takes a dataset as input and produces a directed acyclic graph (DAG) as the output. This problem is known to be NP-hard which is commonly solved using the heuristic methods. There are generally three main approaches to the BNSL problem: score-based , constraint-based and hybrid learning. We propose a new simple and fast algorithm for addressing BNSL problem. The proposed hybrid algorithm is based on a partial ordering learned from data. We reduce the super-exponential search space of structures to the smaller ordering space of nodes. We evaluate the proposed algorithm using some standard benchmark datasets and compare the results with those of some state-of-the-art algorithms. Finally, we show that our algorithm is competitive with recent algorithms.

Cite this Paper


BibTeX
@InProceedings{pmlr-v72-behjati18a, title = {An Order-based Algorithm for Learning Structure of Bayesian Networks}, author = {Behjati, Shahab and Beigy, Hamid}, booktitle = {Proceedings of the Ninth International Conference on Probabilistic Graphical Models}, pages = {25--36}, year = {2018}, editor = {Václav Kratochvíl and Milan Studený}, volume = {72}, series = {Proceedings of Machine Learning Research}, address = {Prague, Czech Republic}, month = {11--14 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v72/behjati18a/behjati18a.pdf}, url = {http://proceedings.mlr.press/v72/behjati18a.html}, abstract = {In this paper, we study the problem learning structure of Bayesian networks from data. The problem of Bayesian networks structure learning (BNSL) takes a dataset as input and produces a directed acyclic graph (DAG) as the output. This problem is known to be NP-hard which is commonly solved using the heuristic methods. There are generally three main approaches to the BNSL problem: score-based , constraint-based and hybrid learning. We propose a new simple and fast algorithm for addressing BNSL problem. The proposed hybrid algorithm is based on a partial ordering learned from data. We reduce the super-exponential search space of structures to the smaller ordering space of nodes. We evaluate the proposed algorithm using some standard benchmark datasets and compare the results with those of some state-of-the-art algorithms. Finally, we show that our algorithm is competitive with recent algorithms.} }
Endnote
%0 Conference Paper %T An Order-based Algorithm for Learning Structure of Bayesian Networks %A Shahab Behjati %A Hamid Beigy %B Proceedings of the Ninth International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2018 %E Václav Kratochvíl %E Milan Studený %F pmlr-v72-behjati18a %I PMLR %J Proceedings of Machine Learning Research %P 25--36 %U http://proceedings.mlr.press %V 72 %W PMLR %X In this paper, we study the problem learning structure of Bayesian networks from data. The problem of Bayesian networks structure learning (BNSL) takes a dataset as input and produces a directed acyclic graph (DAG) as the output. This problem is known to be NP-hard which is commonly solved using the heuristic methods. There are generally three main approaches to the BNSL problem: score-based , constraint-based and hybrid learning. We propose a new simple and fast algorithm for addressing BNSL problem. The proposed hybrid algorithm is based on a partial ordering learned from data. We reduce the super-exponential search space of structures to the smaller ordering space of nodes. We evaluate the proposed algorithm using some standard benchmark datasets and compare the results with those of some state-of-the-art algorithms. Finally, we show that our algorithm is competitive with recent algorithms.
APA
Behjati, S. & Beigy, H.. (2018). An Order-based Algorithm for Learning Structure of Bayesian Networks. Proceedings of the Ninth International Conference on Probabilistic Graphical Models, in PMLR 72:25-36

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