A survey on Bayesian network structure learning from data
Tóm tắt
Từ khóa
Tài liệu tham khảo
Abellán, J., Gómez-Olmedo, M., Moral, S.: Some variations on the PC algorithm. In: Third European Workshop on Probabilistic Graphical Models, pp. 1–8 (2006)
Adel, T., de Campos, C.P.: Learning Bayesian networks with incomplete data by augmentation. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 1684–1690 (2017)
Alonso-Barba, J., de la Ossa, L., Gámez, J., Puerta, J.: Scaling up the greedy equivalence search algorithm by constraining the search space of equivalence classes. Int. J. Approx. Reason. 54, 429–451 (2013)
Alonso-Barba, J.I., de la Ossa, L., Puerta, J.M.: Structural learning of Bayesian networks using local algorithms based on the space of orderings. Soft Comput. 15(10), 1881–1895 (2011)
Alonso, J., de la Ossa, L., Gámez, J., Puerta, J.: On the use of local search heuristics to improve GES-based Bayesian network learning. Appl. Soft Comput. 64, 366–376 (2018)
Bacciu, D., Etchells, T., Lisboa, P., Whittaker, J.: Efficient identification of independence networks using mutual information. Comput. Stat. 28, 621–646 (2013)
Ben-Daya, M., Al-Fawzan, M.: A tabu search approach for the flow shop scheduling problem. Eur. J. Oper. Res. 109(1), 88–95 (1998)
Bøttcher, S.: Learning Bayesian networks with mixed variables. In: Proceedings of the Eighth International Workshop in Artificial Intelligence and Statistics (2001)
Bøttcher, S., Dethlefsen, C.: deal: A package for learning bayesian networks. J. Stat. Softw. 8, 1–40 (2003)
Buntine, W.: Theory refinement on Bayesian networks. In: Proceedings of the 8th Conference on Uncertainty in Artificial Intelligence, pp. 52–60 (1991)
Cheng, J., Bell, D.A., Liu, W.: An algorithm for Bayesian belief network construction from data. In: Proceedings of Artificial Intelligence and Statistics, pp. 83–90 (1997)
Chickering, D.: A transformational characterization of equivalent Bayesian network structures. In: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, pp. 87–98. Morgan Kaufmann (1995)
Chickering, D.M., Heckerman, D., Meek, C.: Large-sample learning of Bayesian networks is NP-Hard. J. Mach. Learn. Res. 5, 1287–1330 (2014)
Colombo, D., Maathuis, M.H.: Order-independent constraint-based causal structure learning. Journal of Machine Learning Research 15, 3741–3782 (2014)
Consortium, Elvira.: Elvira: An environment for creating and using probabilistic graphical models. In: Gámez, J., Salmerón, A. (eds) Proceedings of the First European Workshop on Probabilistic Graphical Models, pp. 222–230 (2002)
Cooper, G.F.: The computational complexity of probabilistic inference using Bayesian belief networks. Artif. Intell. 42, 393–405 (1990)
Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9, 309–347 (1992)
Cussens, J.: Bayesian network learning with cutting planes. In: Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, pp. 153–160 (2011)
Cussens, J., Malone, B., Yuan, C.: IJCAI 2013 tutorial on optimal algorithms for learning Bayesian networks (2013). https://sites.google.com/site/ijcai2013bns/slides . Accessed June 2018
de Campos, C.P., Corani, G., Scanagatta, M., Cuccu, M., Zaffalon, M.: Learning extended tree augmented naive structures. Int. J. Approx. Reason. 68, 153–163 (2015)
de Campos, C.P., Ji, Q.: Efficient structure learning of Bayesian networks using constraints. J. Mach. Learn. Res. 12, 663–689 (2011)
de Campos, C.P., Zeng, Z., Ji, Q.: Structure learning of Bayesian networks using constraints. In: Proceedings of the 26th International Conference on Machine Learning, pp. 113–120 (2009)
Elidan, G., Gould, S.: Learning bounded treewidth Bayesian networks. J. Mach. Learn. Res. 9, 2699–2731 (2008)
Fernández, A., Nielsen, J.D., Salmerón, A.: Learning Bayesian networks for regression from incomplete databases. Int. J. Uncertain. Fuzziness Knowl. Based Syst 18(1), 69–86 (2010)
Fernández, A., Pérez-Bernabé, I., Salmerón, A.: On Using the PC Algorithm for Learning Continuous Bayesian Networks: An Experimental Analysis, CAEPIA’13. Lecture Notes in Computer Science 8109, 342–351 (2013)
Fernández, A., Salmerón, A.: Extension of Bayesian network classifiers to regression problems. In: Geffner, H., Prada, R., Alexandre, I.M., David, N. (eds) Advances in Artificial Intelligence—IBERAMIA 2008, Vol. 5290 of Lecture Notes in Artificial Intelligence, pp. 83–92. Springer (2008)
Friedman, N.: The Bayesian structural EM algorithm. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 129–138 (1998)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29, 131–163 (1997)
Hand, D.J., Yu, K.: Idiot’s Bayes–not so stupid after all? Int. Stat. Rev. 69(3), 385–398 (2001)
He, Y., Jia, J., Geng, Z.: Structural learning of causal networks. Behaviormetrika 44, 287–305 (2017)
Heckerman, D., Geiger, D., Chickering, D.: Learning Bayesian networks: the combination of knowledge and statistical data. Mach. Learn. 20, 197–243 (1995)
Jaakkola, T., Sontag, D., Globerson, A., Meila, M.: Learning Bayesian network structure using LP relaxations. In: Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, pp. 358–365 (2010)
Jaeger, M.: Probabilistic decision graphs—combining verification and ai techniques for probabilistic inference. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 12, 19–42 (2004)
Kalisch, M., Bühlmann, P.: Estimating high-dimensional directed acyclic graphs with the PC-algorithm. J. Mach. Learn. Res. 8, 613–636 (2007)
Koivisto, M.: Parent assignment is hard for the MDL, AIC, and NML costs. In: Proceedings of the 29th Annual Conference On Learning Theory, vol. 4005, pp. 289–303 (2016)
Koivisto, M., Sood, K.: Exact Bayesian structure discovery in Bayesian networks. J. Mach. Learn. Res. 5, 549–573 (2004)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Boston (2009)
Korhonen, J., Parviainen, P.: Exact learning of bounded treewidth Bayesian networks. In: Artificial Intelligence and Statistics, pp 370–378 (2013)
Kwisthout, J. H.P., Bodlaender, H.L., van der Gaag, L.C.: The necessity of bounded treewidth for efficient inference in Bayesian networks. In: Proceedings of the 19th European Conference on Artificial Intelligence, pp. 237–242 (2010)
Lauritzen, S., Wermuth, N.: Graphical models for associations between variables, some of which are qualitative and some quantitative. Ann. Stat. 17, 31–57 (1989)
Lee, C., van Beek, P.: Metaheuristics for score-and-search Bayesian network structure learning. In: Proceedings of the 30th Canadian Conference on Artificial Intelligence, pp. 129–141 (2017)
Madsen, A.L., Jensen, F., Salmerón, A., Langseth, H., Nielsen, T.D.: A parallel algorithm for Bayesian network structure learning from large data sets. Knowl. Based Syst. 117, 46–55 (2017)
Malone, B., Kangas, K., Järvisalo, M., Koivisto, M., Myllymäki, P.: Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction. Mach. Learn. 107, 1–37 (2018)
Malone, B.M.: Learning optimal Bayesian networks with heuristic search. Ph.D. thesis, Mississippi State University (2012)
Moral, S., Rumí, R., Salmerón, A.: Mixtures of Truncated Exponentials in Hybrid Bayesian Networks. In: Benferhat, S., Besnard , P. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Vol. 2143 of Lecture Notes in Artificial Intelligence, pp. 156–167. Springer (2001)
Nie, S., de Campos, C.P., Ji, Q.: Learning bounded treewidth Bayesian networks via sampling. In: Proceedings of the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp. 387–396 (2015)
Nie, S., Mauá, D.D., de Campos, C.P., Ji, Q.: Advances in learning Bayesian networks of bounded treewidth. Adv. Neural Inf. Process. Syst. 27, 2285–2293 (2014)
Nielsen, J.D., Rumí, R., Salmerón, A.: Structural-EM for learning PDG models from incomplete data. Int. J. Approx. Reason. 51(5), 515–530 (2010)
Parviainen, P., Farahani, H.S., Lagergren, J.: Learning bounded treewidth Bayesian networks using integer linear programming. In: Proceedings of the 17th International Conference on Artificial Intelligence and Statistics, pp. 751–759 (2014)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Elsevier, Amsterdam (1988)
Pearl, J.: Causality: models, reasoning and inference. Econom. Theory 19(46), 675–685 (2003)
Pearl, J., Verma, T.S.: A theory of inferred causation. Stud. Logic Found. Math. 134, 789–811 (1995)
Pourret, O., Naïm, P., Marcot, B.: Bayesian Networks: A Practical Guide to Applications. Wiley, Hoboken (2008)
Robinson, R.W.: Counting Labeled Acyclic Digraphs, New Directions in the Theory of Graphs, pp. 28–43. Academic Press, New York (1973)
Romero, V., Rumí, R., Salmerón, A.: Learning hybrid Bayesian networks using mixtures of truncated exponentials. Int. J. Approx. Reason. 42, 54–68 (2006)
Scanagatta, M., Corani, G., de Campos, C.P., Zaffalon, M.: Learning treewidth-bounded Bayesian networks with thousands of variables. Adv. Neural Inf. Process. Syst. 29, 1462–1470 (2016)
Scanagatta, M., Corani, G., de Campos, C.P., Zaffalon, M.: Approximate structure learning for large Bayesian networks. Mach. Learn. 107, 1–19 (2018)
Scanagatta, M., Corani, G., Zaffalon, M.: Improved local search in Bayesian networks structure learning. In:Proceedings of the 3rd International Workshop on Advanced Methodologies for Bayesian Networks, pp. 45–56 (2017)
Scanagatta, M., Corani, G., Zaffalon, M., Yoo, J., Kang, U.: Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets. Int. J. Approx. Reason. 95, 152–166 (2018)
Scanagatta, M., de Campos, C.P., Corani, G., Zaffalon, M.: Learning Bayesian networks with thousands of variables. Adv. Neural Inf. Process. Syst. 28, 1855–1863 (2015)
Scutari, M.: Bayesian network constraint-based structure learning algorithms: Parallel and optimised implementations in the bnlearn R package. CoRR (2014). arXiv:1406.7648
Silander, T., Myllymaki, P.: A simple approach for finding the globally optimal Bayesian network structure. In: Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, pp. 445–452 (2006)
Spirtes, P., Glymour, C.N., Scheines, R.: Causation, Prediction, and Search. MIT Press, Boston (2000)
Steck, H., Tresp, V.: Bayesian belief networks for data mining. University of Magdeburg, pp 145–154 (1996)
Teyssier, M., Koller, D.: Ordering-based search: a simple and effective algorithm for learning Bayesian networks. In: Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, pp. 584–590 (2005)
Yuan, C., Malone, B.: An improved admissible heuristic for learning optimal Bayesian networks. In: Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, pp. 924–933 (2012)
Yuan, C., Malone, B., Wu, X.: Learning optimal Bayesian networks using A* search. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pp. 2186–2191 (2011)
Zheng, X., Aragam, B., Ravikumar, P., Xing, E.: DAGs with no tears: Continuous optimization for structure learning. In: Advances in Neural Information Processing Systems, pp. 9492–9503 (2018)