Categorical Data Analysis, 2002. ,
Local causal and markov blanket induction for causal discovery and feature selection for classification part i: Algorithms and empirical evaluation, Journal of Machine Learning Research, issue.11, pp.171-234, 2010. ,
Incorporating label dependency into the binary relevance framework for multilabel classification, Expert Systems With Applications, pp.1647-1655, 2011. ,
A unified approach to estimation and control of the false discovery rate in bayesian network skeleton identification, European Symposium on Artificial Neural Networks, ESANN, 2011. ,
Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks, Artificial Intelligence in Medicine, vol.54, issue.1, 2012. ,
DOI : 10.1016/j.artmed.2011.09.002
URL : https://hal.archives-ouvertes.fr/hal-00411518
Analysis of lifestyle and metabolic predictors of visceral obesity with Bayesian Networks, BMC Bioinformatics, vol.11, issue.1, p.487, 2010. ,
DOI : 10.1186/1471-2105-11-487
URL : https://hal.archives-ouvertes.fr/inserm-00663887
Determining the direction of causal influence in large probabilistic networks: A constraint-based approach, Proceedings of the Sixteenth European Conference on Artificial Intelligence, pp.263-267, 2004. ,
INFORMATIVE STRUCTURE PRIORS: JOINT LEARNING OF DYNAMIC REGULATORY NETWORKS FROM MULTIPLE TYPES OF DATA, Biocomputing 2005, pp.459-470, 2005. ,
DOI : 10.1142/9789812702456_0044
Top-down induction of clustering trees, International Conference on Machine Learning, ICML, pp.55-63, 1998. ,
Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers, Artificial Intelligence in Medicine, vol.57, issue.3, pp.219-229, 2013. ,
DOI : 10.1016/j.artmed.2012.12.005
Random forests, Machine Learning, pp.5-32, 2001. ,
A strategy for making predictions under manipulation, Journal of Machine Learning Research JMLR, vol.3, pp.35-52, 2008. ,
Theory Refinement on Bayesian Networks, Uncertainty in Artificial Intelligence, UAI, pp.52-60, 1991. ,
DOI : 10.1016/B978-1-55860-203-8.50010-3
Causal and non-causal feature selection for ridge regression, Conference Proceedings, pp.107-128, 2008. ,
Learning Bayesian networks from data: An information-theory based approach, Artificial Intelligence, vol.137, issue.1-2, pp.43-90, 2002. ,
DOI : 10.1016/S0004-3702(02)00191-1
Large-sample learning of Bayesian networks is NP-hard, Journal of Machine Learning Research, JMLR, vol.5, pp.1287-1330, 2004. ,
Optimal structure identification with greedy search, Journal of Machine Learning Research JMLR, vol.3, pp.507-554, 2002. ,
Advances in Bayesian Network Learning using Integer Programming, Uncertainty in Artificial Intelligence, pp.182-191, 2013. ,
On label dependence and loss minimization in multi-label classification, Machine Learning, pp.5-45, 2012. ,
DOI : 10.1007/s10994-012-5285-8
Learning Causal Bayesian Network Structures From Experimental Data, Journal of the American Statistical Association, vol.103, issue.482, pp.778-789, 2008. ,
DOI : 10.1198/016214508000000193
Learning bayesian network structure from massive datasets: The sparse candidate algorithm, Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp.206-215, 1999. ,
Learning bayesian network structure from massive datasets: the " sparse candidate " algorithm, Uncertainty in Artificial Intelligence, UAI, pp.21-30, 1999. ,
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD, pp.58-73, 2012. ,
DOI : 10.1007/978-3-642-33460-3_9
URL : https://hal.archives-ouvertes.fr/hal-01122771
Correlated multi-label feature selection, Proceedings of the 20th ACM international conference on Information and knowledge management, CIKM '11, pp.1087-1096, 2011. ,
DOI : 10.1145/2063576.2063734
Multi-label classification using conditional dependency networks, International Joint Conference on Artificial Intelligence, IJCAI, pp.1300-1305, 2011. ,
Learning bayesian networks: The combination of knowledge and statistical data, Machine Learning, pp.197-243, 1995. ,
Ensembles of Multi-Objective Decision Trees, European Conference on Machine Learning, ECML, pp.624-631, 2007. ,
DOI : 10.1007/978-3-540-74958-5_61
Exact bayesian structure discovery in bayesian networks, Journal of Machine Learning Research, JMLR, vol.5, pp.549-573, 2004. ,
Optimal search on clustered structural constraint for learning bayesian network structure, Journal of Machine Learning Research, issue.11, pp.285-310, 2010. ,
Probabilistic Graphical Models: Principles and Techniques, 2009. ,
Toward optimal feature selection, International Conference on Machine Learning, ICML, pp.284-292, 1996. ,
Classification and regression by randomforest, R News, vol.2, pp.18-22, 2002. ,
Binary relevance efficacy for multilabel classification, Progress in Artificial Intelligence, vol.40, issue.7, pp.303-313, 2012. ,
DOI : 10.1007/s13748-012-0030-x
An extensive experimental comparison of methods for multi-label learning, Pattern Recognition, vol.45, issue.9, pp.3084-3104, 2012. ,
DOI : 10.1016/j.patcog.2012.03.004
Multiple-Instance Learning for Natural Scene Classification, International Conference on Machine Learning, ICML, pp.341-349, 1998. ,
Multi-label text classification with a mixture model trained by em, AAAI Workshop on Text Learning, 1999. ,
Optimal reinsertion: A new search operator for accelerated and more accurate Bayesian network structure learning, International Conference on Machine Learning, ICML, 2003. ,
Bayesian Networks in R: with Applications in Systems Biology, 2013. ,
DOI : 10.1007/978-1-4614-6446-4
Learning Bayesian Networks, 2004. ,
DOI : 10.1016/B978-012370477-1.50021-9
FINDING OPTIMAL MODELS FOR SMALL GENE NETWORKS, Biocomputing 2004, pp.557-567, 2004. ,
DOI : 10.1142/9789812704856_0052
Towards scalable and data efficient learning of Markov boundaries, International Journal of Approximate Reasoning, vol.45, issue.2, pp.211-232, 2007. ,
DOI : 10.1016/j.ijar.2006.06.008
Growing Bayesian network models of gene networks from seed genes, Bioinformatics, vol.21, issue.Suppl 2, pp.224-229, 2005. ,
DOI : 10.1093/bioinformatics/bti1137
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1988. ,
Learning gaussian graphical models of gene networks with false discovery rate control, European Conference on Evolutionary Computation, pp.165-176, 2008. ,
Finding consensus bayesian network structures, Journal of Artificial Intelligence Research, vol.42, pp.661-687, 2012. ,
Finding optimal bayesian network given a super-structure, Journal of Machine Learning Research , JMLR, vol.9, pp.2251-2286, 2008. ,
Inferring subnetworks from perturbed expression profiles, Bioinformatics, vol.17, issue.Suppl 1, pp.215-224, 2001. ,
DOI : 10.1093/bioinformatics/17.suppl_1.S215
Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours, Computers in Biology and Medicine, pp.334-341, 2013. ,
DOI : 10.1016/j.compbiomed.2012.12.002
URL : https://hal.archives-ouvertes.fr/hal-00851231
R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing Vienna, 2013. ,
Classifier Chains for Multi-label Classification, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD, pp.254-269, 2009. ,
DOI : 10.1007/978-3-642-04174-7_17
An efficient learning algorithm for local bayesian network structure discovery, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD, pp.164-169, 2010. ,
A novel Markov boundary based feature subset selection algorithm, Neurocomputing, vol.73, issue.4-6, pp.578-584, 2010. ,
DOI : 10.1016/j.neucom.2009.05.018
URL : https://hal.archives-ouvertes.fr/hal-00383776
Improved functional prediction of proteins by learning kernel combinations in multilabel settings, BMC Bioinformatics, vol.8, issue.Suppl 2, p.12, 2007. ,
DOI : 10.1186/1471-2105-8-S2-S12
Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978. ,
DOI : 10.1214/aos/1176344136
Learning bayesian networks with the bnlearn R package, Journal of Statistical Software, vol.35, pp.1-22, 2010. ,
Measures of Variability for Graphical Models, Ph.D. thesis School in Statistical Sciences, 2011. ,
Bayesian Network Structure Learning with Permutation Tests, Communications in Statistics - Theory and Methods, vol.35, issue.3, pp.3233-3243, 2012. ,
DOI : 10.1007/s10994-006-6889-7
Identifying significant edges in graphical models of molecular networks, Artificial Intelligence in Medicine, vol.57, issue.3, pp.207-217, 2013. ,
DOI : 10.1016/j.artmed.2012.12.006
A Simple Approach for Finding the Globally Optimal Bayesian Network Structure, Uncertainty in Artificial Intelligence, UAI, pp.445-452, 2006. ,
The challenge problem for automated detection of 101 semantic concepts in multimedia, Proceedings of the 14th annual ACM international conference on Multimedia , MULTIMEDIA '06, pp.421-430, 2006. ,
DOI : 10.1145/1180639.1180727
Causation, Prediction , and Search, 2000. ,
DOI : 10.1007/978-1-4612-2748-9
A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach, Electronic Notes in Theoretical Computer Science, vol.292, pp.135-151, 2013. ,
DOI : 10.1016/j.entcs.2013.02.010
Learning Bayesian network structure: Towards the essential graph by integer linear programming tools, International Journal of Approximate Reasoning, vol.55, issue.4, pp.1043-1071, 2014. ,
DOI : 10.1016/j.ijar.2013.09.016
Algorithms for large scale Markov blanket discovery, Florida Artificial Intelligence Research Society Conference FLAIRS'03, pp.376-381, 2003. ,
Permutation Testing Improves Bayesian Network Learning, European Conference on Machine Learning and Knowledge Discovery in Databases, ECML- PKDD, pp.322-337, 2010. ,
DOI : 10.1007/978-3-642-15939-8_21
The max-min hill-climbing Bayesian network structure learning algorithm, Machine Learning, pp.31-78, 2006. ,
DOI : 10.1007/s10994-006-6889-7
Bounding the false discovery rate in local Bayesian network learning, AAAI Conference on Artificial Intelligence, pp.1100-1105, 2008. ,
Mining Multilabel Data. Transformation, pp.1-20, 2010. ,
Random klabelsets for Multi-Label Classification, IEEE Transactions on Knowledge and Data Engineering TKDE, vol.23, pp.1-12, 2010. ,
Random k-Labelsets: An Ensemble Method for Multilabel Classification, Proceedings of the 18th European Conference on Machine Learning, pp.406-417, 2007. ,
DOI : 10.1007/978-3-540-74958-5_38
Optimized algorithm for learning bayesian network superstructures, International Conference on Pattern Recognition Applications and Methods, ICPRAM, 2012. ,
Efficient methods for learning Bayesian network super-structures, Neurocomputing, vol.123, pp.3-12, 2014. ,
DOI : 10.1016/j.neucom.2012.10.035
Multi-label learning by exploiting label dependency, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, p.999, 2010. ,
DOI : 10.1145/1835804.1835930
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization, IEEE Transactions on Knowledge and Data Engineering, vol.18, issue.10, pp.1338-1351, 2006. ,
DOI : 10.1109/TKDE.2006.162