CRTI - Centre de Recherche en Technologie de l'Information
Disciplines :
Computer science
Author, co-author :
Benmouna, Youcef
Benazzouz, Mourtada
Chikh, Mohammed Amine
Mahmoudi, Said ; Université de Mons > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle
Language :
English
Title :
New Method for Bayesian Network Learning
Publication date :
27 August 2018
Journal title :
International Journal of Pattern Recognition and Artificial Intelligence
ISSN :
0218-0014
Publisher :
World Scientific Publishing Co., Singapore
Peer reviewed :
Peer Reviewed verified by ORBi
Research unit :
F114 - Informatique, Logiciel et Intelligence artificielle
Research institute :
R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique R450 - Institut NUMEDIART pour les Technologies des Arts Numériques
H. Akaike, A new look at the statistical model identification, IEEE Trans. Automat. Control 19(6) (1974) 716-723.
A. Asuncion and D. Newman, UCI machine learning repository, http://www. ics. uci. edu/ -mlearn/MLRepository. html (2007).
H. Bouhamed et al., A new learning structure heuristic of Bayesian networks from data, Machine Learning and Data Mining in Pattern Recognition (Springer, Berlin, 2012), pp. 183-197.
R. R. Bouckaert, Bayesian belief networks: From construction to inference, doctoral dissertation, University Utrecht (2001).
D. M. Chickering, D. Heckerman and C. Meek, Large-sample learning of Bayesian networks is NP-hard, J. Mach. Learn. Res. 5 (2004) 1287-1330.
C. K. Chow and C. N. Liu, Approximating discrete probability distributions with dependence trees, IEEE Trans. Inf. Theory 14(3) (1968) 462-467.
G. F. Cooper and E. Herskovits, A Bayesian method for the induction of probabilistic networks from data, Mach. Learn. 9(4) (1992) 309-347.
J. Cussens, Bayesian network learning with cutting planes, arXiv:1202. 3713.
J. Cussens, Integer programming for Bayesian network structure learning, Qual. Technol. Quant. Manage. 11(1) (2014) 99-110.
R. Daly and Q. Shen, Learning Bayesian network equivalence classes with ant colony optimization, J. Artif. Intell. Res. 35(1) (2009) 391.
C. P. De Campos and Q. Ji, Efficient structure learning of Bayesian networks using constraints, J. Mach. Learn. Res. 12 (2011) 663-689.
C. P. De Campos, Z. Zeng and Q. Ji, Structure learning of Bayesian networks using constraints, in Proc. 26th Annual Int. Conf. Machine Learning (ACM, New York, 2009), pp. 113-120.
E. B. Dos Santos, E. R. Hruschka Jr., E. R. Hruschka and N. F. Ebecken, A distancebased mutation operator for learning bayesian network structures using evolutionary algorithms, in 2010 IEEE Congress on Evolutionary Computation (CEC) (IEEE, New York, 2010), pp. 1-8.
X. Fan, B. Malone and C. Yuan, Finding optimal Bayesian network structures with constraints learned from data, in Proc. 30th Annual Conf. Uncertainty in Artificial Intelligence (UAI-14) (2014), pp. 200-209.
X. Fan and C. Yuan, An improved lower bound for Bayesian network structure learning, in AAAI (AAAI Press, 2015), pp. 3526-3532.
X. Fan, C. Yuan and B. M. Malone, Tightening bounds for Bayesian network structure learning, in AAAI (AAAI Press, 2014), pp. 2439-2445.
E. Faulkner, K2GA: Heuristically guided evolution of Bayesian network structures from data, in 2007 IEEE Symp. Computational Intelligence and Data Mining (IEEE, New York, 2007), pp. 18-25.
A. K. Gavai, Y. Tikunov, R. Ursem, A. Bovy, F. van Eeuwijk, H. Nijveen and J. A. Leunissen, Constraint-based probabilistic learning of metabolic pathways from tomato volatiles, Metabolomics 5(4) (2009) 419-428.
M. Kalisch and P. Buhlmann, Estimating high-dimensional directed acyclic graphs with the PC-algorithm, J. Mach. Learn. Res. 8 (2007) 613-636.
M. Koivisto and K. Sood, Exact Bayesian structure discovery in Bayesian networks, J. Mach. Learn. Res. 5 (2004) 549-573.
C. Lee and P. Van Beek, An experimental analysis of anytime algorithms for Bayesian network structure learning, in Advanced Methodologies for Bayesian Networks (PMLR, 2017), pp. 69-80.
Z. Liu, B. Malone and C. Yuan, Empirical evaluation of scoring functions for Bayesian network model selection, BMC Bioinformatics 13(15) (2012) S14.
F. Liu and Q. Zhu, The max-relevance and min-redundancy greedy Bayesian network learning algorithm, in Bio-inspired Modeling of Cognitive Tasks (Springer, Berlin, Heidelberg, 2007), pp. 346-356.
B. M. Malone, C. Yuan and E. A. Hansen, Memory-efficient dynamic programming for learning optimal Bayesian networks, in AAAI (AAAI Press, 2011), pp. 1057-1062.
B. Malone, C. Yuan, E. Hansen and S. Bridges, Improving the scalability of optimal Bayesian network learning with external-memory frontier breadth-first branch and bound search, in Proc. 27th Conf. Uncertainty in Artificial Intelligence (AUAI Press, Arlington, 2011), pp. 479-488.
M. Meila and T. Jaakkola, Tractable Bayesian learning of tree belief networks, in Proc. Sixteenth Conf. Uncertainty in Artificial Intelligence (Morgan Kaufmann Publishers Inc., 2000), pp. 380-388.
S. Oliver and S. Gholami, Locally consistent Bayesian network scores for multi-relational data, in Proc. 26th Int. Joint Conf. Artificial Intelligence (AAAI Press, 2017), pp. 2693-2700.
E. Perrier, S. Imoto and S. Miyano, Finding optimal Bayesian network given a superstructure, J. Mach. Learn. Res. 9(2) (2008) 2251-2286.
R. W. Robinson, Counting unlabeled acyclic digraphs, in Combinatorial Mathematics V, Vol. 622 (Springer, Berlin, 1977), pp. 28-43.
T. Silander and P. Myllymaki, A simple approach for finding the globally optimal Bayesian network structure, arXiv:1206. 6875.
A. P. Singh and A. W. Moore, Finding optimal Bayesian networks by dynamic programming, Centre for Automated Learning and Discovery (2005).
G. Schwarz, Estimating the dimension of a model, Ann. Stat. 6(2) (1978) 461-464, available at http://dx. doi. org/10. 1214/aos/1176344136.
P. Spirtes and C. Glymour, An algorithm for fast recovery of sparse causal graphs, Soc. Sci. Comput. Rev. 9(1) (1991) 62-72.
M. Teyssier and D. Koller, Ordering-based search: A simple and effective algorithm for learning Bayesian networks, arXiv:1207. 1429.
P. Van Beek and H. F. Hoffmann, Machine learning of Bayesian networks using constraint programming, in Principles and Practice of Constraint Programming (Springer International Publishing, Cham, 2015), pp. 429-445.
C. Yuan and B. Malone, An improved admissible heuristic for finding optimal Bayesian networks, in Proc. 28th Conf. Uncertainty in Artificial Intelligence (AUAI Press, Arlington, 2012), pp. 924-933.
C. Yuan and B. Malone, Learning optimal Bayesian networks: A shortest path perspective, J. Artif. Intell. Res. 48 (2013) 23-65.
C. Yuan, B. Malone and X. Wu, Learning optimal Bayesian networks using A search, in Proc. Int. Joint Conf. Artificial Intelligence, Vol. 22 (AAAI Press, 2011), p. 2186.
Q. Zhang, Z. Li, C. J. Zhou and X. P. Wei, Bayesian network structure learning based on the chaotic particle swarm optimization algorithm, Genet. Mol. Res. 12(4) (2013) 4468-4479.
