@INPROCEEDINGs{Langley, title="An Analysis of {B}ayesian Classifiers", pages={223-228}, year={1992}, BOOKtitle={Proc. of the 10th National Conf. on AI}, url={http://www.informatik.uni-trier.de/~ley/db/conf/aaai/aaai92.html#LangleyIT92}, author={P. Langley and W. Iba and K. Thompson} } @BOOK{DudaHartCV, author = {Duda, R. O. and Hart, P. E. and Stork, D. G. }, citeulike-ARTICLE-id = {3451700}, keywords = {classification, pattern}, posted-at = {2008-10-26 19:03:35}, priority = {0}, publisher = {Wiley NY}, title = {{Pattern classification and scene analysis}}, year = {1973} } @INPROCEEDINGs{96beyond, author = {Domingos, Pedro and Pazzani, Michael }, BOOKtitle = {Proc. of the 13th Int. Conf. on Machine Learning}, citeulike-ARTICLE-id = {3416068}, keywords = {machine-learning}, pages = {105--112}, posted-at = {2008-10-15 23:38:23}, priority = {2}, title = {Beyond Independence: Conditions for the Optimality of the Simple {B}ayesian Classifier}, url = {http://www.cs.washington.edu/homes/pedrod/papers/mlc96.pdf}, year = {1996} } @INPROCEEDINGs{ZhengWebb05, author = {F. Zheng and G.I. Webb}, title = "{A Comparative Study of Semi-naive {B}ayes Methods in Classification Learning}", BOOKtitle = {Proc. of the 4th Australian Data Mining Conf.}, year = {2005}, editor = {S.J. Simoff and G.J. Williams and J. Galloway and I. Kolyshkina}, pages = {141-156}, keywords = {AODE, Conditional Probability Estimation} } @ARTICLE{Zheng+WebbLL, author = {Zheng, Z. and Webb, G. I. }, title = "{Lazy Learning of {B}ayesian Rules}", journal = {Mach. Learn.}, volume = {41}, number = {1}, year = {2000}, issn = {0885-6125}, pages = {53--84}, doi = {http://dx.doi.org/10.1023/A:1007613203719}, publisher = {Kluwer Academic Publishers}, address = {Hingham, MA, USA}, } @INPROCEEDINGs{Keogh+Pazzani, author = "Keogh, E. and Pazzani, M.", title = "Learning Augmented {B}ayesian Classifiers: A Comparison of Distribution-based and Classification-based Approaches", BOOKtitle = "Proc. of the 7th Int. Workshop on AI and Statistics", year = "1999", pages = "225-230", } @ARTICLE{Webb+Boughton+Wang, author = {Webb, G. I. and Boughton, J. R. and Wang, Z.}, title = "Not {S}o {N}aive {B}ayes: {A}ggregating {O}ne-{D}ependence {E}stimators", journal = {Mach. Learn.}, volume = {58}, number = {1}, year = {2005}, issn = {0885-6125}, pages = {5-24}, doi = {http://dx.doi.org/10.1007/s10994-005-4258-6}, publisher = {Kluwer Academic Publishers}, address = {Hingham, MA, USA}, } @INPROCEEDINGs{Sahami, author = {Sahami, M.}, title = "Learning limited dependence {B}ayesian classifiers", BOOKtitle = {Proc. of the 2nd Int. Conf. on Knowledge Discovery in Databases}, pages = {335-338}, year = {1996}, } @ARTICLE{lauritzen1989graphicalmodels, author = {Lauritzen, S. L. and Wermuth, N.}, doi = {doi:10.1214/aos/1176347003}, journal = {Annals of Statistics}, number = {1}, pages = {31--57}, title = {Graphical Models for Associations between Variables, some of which are Qualitative and some Quantitative}, url = {http://projecteuclid.org/DPubS?service=UI\&\#38;version=1.0\&\#38;verb=Display\&\#38;handle=euclid.aos/1176347003}, volume = {17}, year = {1989} } @ARTICLE{Lauritzen92propagationof, author = {S. L. Lauritzen}, title = {Propagation of probabilities, means and variances in mixed graphical association models}, journal = {Journal of the American Statistical Association}, year = {1992}, volume = {87}, pages = {1098--1108} } @ARTICLE{599407, author = {S. L. Lauritzen and F. Jensen}, title = "Stable local computation with conditional {G}aussian distributions", journal = {Statistics and Computing}, volume = {11}, number = {2}, year = {2001}, issn = {0960-3174}, pages = {191--203}, publisher = {Kluwer Academic Publishers}, address = {Hingham, MA, USA}, } @ARTICLE{628451, author = {K. G. Olesen}, title = {Causal Probabilistic Networks with Both Discrete and Continuous Variables}, journal = {IEEE Trans. Pattern Anal. Mach. Intell.}, volume = {15}, number = {3}, year = {1993}, issn = {0162-8828}, pages = {275--279}, doi = {http://dx.doi.org/10.1109/34.204909}, publisher = {IEEE Computer Society}, address = {Washington, DC, USA}, } @BOOK{DeGroot, author = {M. H. DeGroot}, title = {Optimal Statistical Decisions}, publisher = {McGraw-Hill}, address = {New York}, year = {1970} } @TECHREPORT{naga99optimization, author = "P. Larra{\~n}aga and R. Etxeberria and J. Lozano and J. M. Pe{\~n}a", title = "Optimization by learning and simulation of {B}ayesian and {G}aussian networks", key = "EHU-KZAA-IK-4/99", institution = " University of the Basque Country", year = "1999" } @INPROCEEDINGs{85336, author = {Andersen, Stig K. and Olesen, Kristian G. and Jensen, Finn V. and Jensen, Frank}, title = "{HUGIN}--{A} Shell for Building {B}ayesian Belief universes for Expert Systems", BOOKtitle = {Proc. of the 11th Int. Joint Conf. on AI}, year = 1989, pages = "1080-1085", } @MISC{Waikato, key = {Weka08}, author = {Weka}, title = {Collection of Datasets avalaibles from the Weka Official HomePage}, note = {\url{http://www.cs.waikato.ac.nz/ml/weka/}}, institution = "University of Waikato, ", year = {2008} } @BOOK{Weka, author = "Witten, Ian H. and Frank, Eibe", title = "Data Mining: Practical Machine Learning Tools and Techniques", year = "2005", publisher = "Morgan Kaufmann", edition = "2", keywords = "ML,BOOK.ml", url = "/bib/private/witten/Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann.pdf", } @ARTICLE{dietterich98approximate, author = "T. G. Dietterich", title = "Approximate Statistical Test For Comparing Supervised Classification Learning Algorithms", journal = "Neural Comput.", volume = "10", number = "7", pages = "1895-1923", year = "1998", url = "citeseer.ist.psu.edu/dietterich98approximate.html" } @ARTICLE{PLI2, author = {Aritz P\'erez and Pedro Larra{\~n}aga and I{\~n}aki Inza}, title = {Supervised classification with conditional gaussian networks: Increasing the structure complexity from naive {B}ayes}, journal = {Int. J. Approx. Reasoning}, year = {2006}, volume = {43}, pages = {1-25} } @INPROCEEDINGs{GN, author = {Dan Geiger and David Heckerman}, title = "Learning {G}aussian Networks", BOOKtitle = {Proc. of the 10th Annual Conf. on Uncertainty in AI}, year = {1994}, pages = {235-243}, } @INPROCEEDINGs{695921, author = {S. Moral and R. Rum\'{\i} and A. Salmer\'{o}n}, title = "Mixtures of {T}runcated {E}xponentials in Hybrid {B}ayesian Networks", BOOKtitle = {Proc. of the 6th European Conf. on Symbolic and Quantitative Approaches to Reasoning with Uncertainty}, year = {2001}, isbn = {3-540-42464-4}, pages = {156--167}, } @ARTICLE{1327436, author = {Xindong Wu and Vipin Kumar and J. Ross Quinlan and Joydeep Ghosh and Qiang Yang and Hiroshi Motoda and Geoffrey J. McLachlan and Angus Ng and Bing Liu and Philip S. Yu and Zhi-Hua Zhou and Michael Steinbach and David J. Hand and Dan Steinberg}, title = {Top 10 algorithms in data mining}, journal = {Knowl. Inf. Syst.}, volume = {14}, number = {1}, year = {2007}, issn = {0219-1377}, pages = {1--37}, doi = {http://dx.doi.org/10.1007/s10115-007-0114-2}, publisher = {Springer-Verlag New York, Inc.}, address = {New York, NY, USA}, } @BOOK{Neapolitan, author = {Neapolitan, Richard E. }, citeulike-ARTICLE-id = {106361}, howpublished = {Hardcover}, isbn = {0130125342}, keywords = {bayesian, networks}, month = {April}, posted-at = {2008-06-19 13:18:42}, priority = {2}, publisher = {{Prentice Hall}}, title = {Learning {B}ayesian Networks}, url = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&path=ASIN/0130125342}, year = {2003} } @MISC{Asuncion+Newman2007, author = "A. Asuncion and D.J. Newman", year = "2007", title = "{UCI} Machine Learning Repository", note= {University of California, Irvine, School of Information and Computer Sciences. \url{http://www.ics.uci.edu/~mlearn/MLRepository.html}.} } @INPROCEEDINGs{Fayyad1993, author = {Usama M. Fayyad and Keki B. Irani}, BOOKtitle = {Proc. of the 13th Int. Joint Conf. on Articial Intelligence}, pages = {1022-1027}, title = {Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning}, url = {http://www.cs.orst.edu/\~{}bulatov/papers/fayyad-discretization.pdf}, year = {1993} } @ARTICLE{Alpaydin, abstract = {Dietterich (1998) reviews five statistical tests and proposes the 5 x 2 cv t test for determining whether there is a significant difference between the error rates of two classifiers. In our experiments, we noticed that the 5 x 2 cv t test result may vary depending on factors that should not affect the test, and we propose a variant, the combined 5 x 2 cv F test, that combines multiple statistics to get a more robust test. Simulation results show that this combined version of the test has lower type I error and higher power than 5 x 2 cv proper.}, author = {Alpaydin, E. }, citeulike-ARTICLE-id = {4235160}, issn = {0899-7667}, journal = {Neural Comput.}, keywords = {classifier, comparison, machinelearning, pt}, month = {November}, number = {8}, pages = {1885--1892}, posted-at = {2009-03-29 15:49:32}, priority = {0}, title = {Combined 5 x 2 cv F test for comparing supervised classification learning algorithms.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/10578036}, volume = {11}, year = {1999} } @INPROCEEDINGs{discretization, abstract = {Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization, identify defining characteristics of the methods, and conduct an empirical evaluation of several methods. We compare binning, an unsupervised discretization method, to entropy-based and purity-based methods, which are supervised algorithms. We found that the performance of the Naive-Bayes algorithm significantly improved when features were discretized using an entropy-based method. In fact, over the 16 tested datasets, the discretized version of Naive-Bayes slightly outperformed C4.5 on average. We also show that in some cases, the performance of the C4.5 induction algorithm significantly improved if features were discretized in advance; in our experiments, the performance never significantly degraded, an interesting phenomenon considering the fact that C4.5 is capable of locally discretizing features.}, author = {Dougherty, James and Kohavi, Ron and Sahami, Mehran }, BOOKtitle = {Proc. of the Twelfth Int. Conf. on Machine Learning}, citeulike-ARTICLE-id = {3459315}, editor = {Prieditis, Armand and Russell, Stuart }, keywords = {machine-learning}, location = {Tahoe City, California, USA}, pages = {194--202}, posted-at = {2008-10-29 04:01:06}, priority = {2}, title = {Supervised and Unsupervised Discretization of Continuous Features}, url = {http://citeseer.ist.psu.edu/109288.html}, year = {1995} } @ARTICLE{Demsar, address = {Cambridge, MA, USA}, author = {Dem\v{s}ar, Janez }, citeulike-ARTICLE-id = {2087990}, issn = {1533-7928}, journal = {J. Mach. Learn. Res.}, keywords = {analysis, classifier, evaluation, machine-learning}, pages = {1--30}, posted-at = {2009-04-03 09:23:22}, priority = {5}, publisher = {MIT Press}, title = {Statistical Comparisons of Classifiers over Multiple Data Sets}, url = {http://portal.acm.org/citation.cfm?id=1248547.1248548}, volume = {7}, year = {2006} } @ARTICLE{GarciaHerrera09, author = {Salvador Garc\'{i}a and Francisco Herrera}, interHash = {fde72ae359d332a4483d9e51b056db6b}, intraHash = {b4d7c48052aff2265db89d77d43dfabc}, journal = {J. Mach. Learn. Res.}, pages = {2677--2694}, title = {An Extension on ``Statistical Comparisons of Classifiers over Multiple Data Sets'' for all Pairwise Comparisons }, url = {http://www.jmlr.org/papers/volume9/garcia08a/garcia08a.pdf}, volume = {9}, year = {2009}, abstract = {In a recently published paper in JMLR, Demšar (2006) recommends a set of non-parametric statistical tests and procedures which can be safely used for comparing the performance of classifiers over multiple data sets. After studying the paper, we realize that the paper correctly introduces the basic procedures and some of the most advanced ones when comparing a control method. However, it does not deal with some advanced topics in depth. Regarding these topics, we focus on more powerful proposals of statistical procedures for comparing n × n classifiers. Moreover, we illustrate an easy way of obtaining adjusted and comparable p-values in multiple comparison procedures.} } @inproceedings{GAODEs, author = {M. Julia Flores and Jos{\'e} A. G{\'a}mez and Ana M. Mart\'{\i}nez and Jose Miguel Puerta}, title = {GAODE and HAODE: two proposals based on AODE to deal with continuous variables}, booktitle = {ICML}, year = {2009}, pages = {40}, ee = {http://doi.acm.org/10.1145/1553374.1553414}, crossref = {DBLPconf/icml/2009} } @proceedings{DBLPconf/icml/2009, editor = {Andrea Pohoreckyj Danyluk and L{\'e}on Bottou and Michael L. Littman}, title = {Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009}, booktitle = {ICML}, publisher = {ACM}, series = {ACM International Conference Proceeding Series}, volume = {382}, year = {2009}, isbn = {978-1-60558-516-1} } @inproceedings{DiscreCAEPIA09, author = {M. Julia Flores and Jos{\'e} A. G{\'a}mez and Ana M. Mart\'{\i}nez and Jose Miguel Puerta}, title = {Estudio y Comparativa de Diferentes Discretizaciones en Clasificadores Bayesianos}, pages = {265-274}, booktitle = {CAEPIA'09: Thirteenth Spanish Conference for Artificial Intelligence, Sevilla, Spain, November 9-14}, year = {2009}, }