Discrete Bayesian Network Classifiers
暂无分享,去创建一个
[1] Fengzhan Tian,et al. A Discriminative Learning Method of TAN Classifier , 2007, ECSQARU.
[2] Dirk Van den Poel,et al. Random Multiclass Classification: Generalizing Random Forests to Random MNL and Random NB , 2007, DEXA.
[3] Geoffrey I. Webb,et al. Efficient lazy elimination for averaged one-dependence estimators , 2006, ICML.
[4] Shigeo Abe DrEng. Pattern Classification , 2001, Springer London.
[5] Estevam R. Hruschka,et al. Bayesian network classifiers: Beyond classification accuracy , 2011, Intell. Data Anal..
[6] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[7] Estevam R. Hruschka,et al. Towards efficient variables ordering for Bayesian networks classifier , 2007, Data Knowl. Eng..
[8] Richard E. Neapolitan,et al. Learning Bayesian networks , 2007, KDD '07.
[9] Pedro Larrañaga,et al. Discriminative Learning of Bayesian Network Classifiers via the TM Algorithm , 2005, ECSQARU.
[10] Pedro Larrañaga,et al. Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS , 2005, J. Biomed. Informatics.
[11] Terran Lane,et al. Learning class-discriminative dynamic Bayesian networks , 2005, ICML.
[12] Marie-France Sagot,et al. Efficient Learning of Bayesian Network Classifiers , 2007, Australian Conference on Artificial Intelligence.
[13] Mark A. Hall,et al. Correlation-based Feature Selection for Machine Learning , 2003 .
[14] Bin Shen,et al. Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers , 2002, Machine Learning.
[15] Pedro Larrañaga,et al. Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data , 2001, Artif. Intell. Medicine.
[16] Sebastian Tschiatschek,et al. Maximum Margin Bayesian Network Classifiers , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] C. N. Liu,et al. Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.
[18] Michael I. Jordan,et al. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.
[19] Harry Zhang,et al. Naive Bayes for optimal ranking , 2008, J. Exp. Theor. Artif. Intell..
[20] Tzu-Tsung Wong. Alternative prior assumptions for improving the performance of naïve Bayesian classifiers , 2008, Data Mining and Knowledge Discovery.
[21] Martin Mozina,et al. Nomograms for Visualization of Naive Bayesian Classifier , 2004, PKDD.
[22] Tony Jebara,et al. Machine learning: Discriminative and generative , 2006 .
[23] Basilio Sierra,et al. Histogram distance-based Bayesian Network structure learning: A supervised classification specific approach , 2009, Decis. Support Syst..
[24] Jeff A. Bilmes,et al. A Submodular-supermodular Procedure with Applications to Discriminative Structure Learning , 2005, UAI.
[25] Mehran Sahami,et al. Learning Limited Dependence Bayesian Classifiers , 1996, KDD.
[26] G. Niklas Norén,et al. Case Based Imprecision Estimates for Bayes Classifiers with the Bayesian Bootstrap , 2005, Machine Learning.
[27] Robert E. Tarjan,et al. Fibonacci heaps and their uses in improved network optimization algorithms , 1984, JACM.
[28] Peter J. F. Lucas,et al. Employing Maximum Mutual Information for Bayesian Classification , 2004, ISBMDA.
[29] Michael G. Madden. A New Bayesian Network Structure for Classification Tasks , 2002, AICS.
[30] Gregory F. Cooper,et al. Model Averaging for Prediction with Discrete Bayesian Networks , 2004, J. Mach. Learn. Res..
[31] Juan José Rodríguez Diez,et al. Naïve Bayes Ensembles with a Random Oracle , 2007, MCS.
[32] Constantin F. Aliferis,et al. Algorithms for Large Scale Markov Blanket Discovery , 2003, FLAIRS.
[33] Russell Greiner,et al. Discriminative Model Selection for Belief Net Structures , 2005, AAAI.
[34] Xiaoyi Jiang,et al. Structure identification of Bayesian classifiers based on GMDH , 2009, Knowl. Based Syst..
[35] Eyke Hüllermeier,et al. On Pairwise Naive Bayes Classifiers , 2007, ECML.
[36] Liangxiao Jiang,et al. Weighted average of one-dependence estimators† , 2012, J. Exp. Theor. Artif. Intell..
[37] Moninder Singh,et al. Construction of Bayesian network structures from data: A brief survey and an efficient algorithm , 1995, Int. J. Approx. Reason..
[38] Jose Miguel Puerta,et al. HODE: Hidden One-Dependence Estimator , 2009, ECSQARU.
[39] Constantin F. Aliferis,et al. HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection , 2003, AMIA.
[40] Geoffrey I. Webb,et al. Not So Naive Bayes: Aggregating One-Dependence Estimators , 2005, Machine Learning.
[41] Pedro Larrañaga,et al. Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches , 1998, Artif. Intell. Medicine.
[42] Qiang Yang,et al. Test-cost sensitive naive Bayes classification , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).
[43] Franz Pernkopf,et al. Stochastic margin-based structure learning of Bayesian network classifiers , 2013, Pattern Recognit..
[44] Peter J. F. Lucas,et al. Restricted Bayesian Network Structure Learning , 2002, Probabilistic Graphical Models.
[45] D. Titterington,et al. Comparison of Discrimination Techniques Applied to a Complex Data Set of Head Injured Patients , 1981 .
[46] Sebastian Thrun,et al. Bayesian Network Induction via Local Neighborhoods , 1999, NIPS.
[47] Constantin F. Aliferis,et al. Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation , 2010, J. Mach. Learn. Res..
[48] Thomas Richardson,et al. Interpretable Boosted Naïve Bayes Classification , 1998, KDD.
[49] Franz Pernkopf,et al. Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers , 2010, J. Mach. Learn. Res..
[50] Russell Greiner,et al. Budgeted Learning of Naive-Bayes Classifiers , 2003, UAI.
[51] Joseph F. Murray,et al. Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application , 2005, J. Mach. Learn. Res..
[52] Irving John Good,et al. The Estimation of Probabilities: An Essay on Modern Bayesian Methods , 1965 .
[53] Eugene Santos,et al. Exploring Case-Based Bayesian Networks and Bayesian Multi-nets for Classification , 2004, Canadian Conference on AI.
[54] Concha Bielza,et al. Forward stagewise naïve Bayes , 2011, Progress in Artificial Intelligence.
[55] Byoung-Tak Zhang,et al. Bayesian model averaging of Bayesian network classifiers over multiple node-orders: application to sparse datasets , 2005, IEEE Trans. Syst. Man Cybern. Part B.
[56] A. J. Feelders,et al. Discriminative Scoring of Bayesian Network Classifiers: a Comparative Study , 2006, Probabilistic Graphical Models.
[57] Kaizhu Huang,et al. Discriminative training of Bayesian Chow-Liu multinet classifiers , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..
[58] Dunja Mladenic,et al. Feature Selection for Unbalanced Class Distribution and Naive Bayes , 1999, ICML.
[59] Gregory M. Provan,et al. Efficient Learning of Selective Bayesian Network Classifiers , 1996, ICML.
[60] Geoffrey I. Webb,et al. Ensemble Selection for SuperParent-One-Dependence Estimators , 2005, Australian Conference on Artificial Intelligence.
[61] Nir Friedman,et al. Learning Belief Networks in the Presence of Missing Values and Hidden Variables , 1997, ICML.
[62] David Heckerman,et al. Knowledge Representation and Inference in Similarity Networks and Bayesian Multinets , 1996, Artif. Intell..
[63] Concha Bielza,et al. Cost-sensitive selective naive Bayes classifiers for predicting the increase of the h-index for scientific journals , 2014, Neurocomputing.
[64] Luis M. de Campos,et al. Learning Bayesian Network Classifiers: Searching in a Space of Partially Directed Acyclic Graphs , 2005, Machine Learning.
[65] Constantin F. Aliferis,et al. Towards Principled Feature Selection: Relevancy, Filters and Wrappers , 2003 .
[66] A. P. Dawid,et al. Generative or Discriminative? Getting the Best of Both Worlds , 2007 .
[67] Enrico Fagiuoli,et al. Tree-Based Credal Networks for Classification , 2003, Reliab. Comput..
[68] Chun-Nan Hsu,et al. Bayesian classification for data from the same unknown class , 2002, IEEE Trans. Syst. Man Cybern. Part B.
[69] Geoffrey I. Webb,et al. Lazy Learning of Bayesian Rules , 2000, Machine Learning.
[70] Franz Pernkopf,et al. Floating search algorithm for structure learning of Bayesian network classifiers , 2003, Pattern Recognit. Lett..
[71] Mohak Shah,et al. Evaluating Learning Algorithms: A Classification Perspective , 2011 .
[72] Zijian Zheng,et al. Naive Bayesian Classifier Committees , 1998, ECML.
[73] Paola Sebastiani,et al. c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. Robust Learning with Missing Data , 2022 .
[74] Andrés R. Masegosa,et al. Methods to Determine the Branching Attribute in Bayesian Multinets Classifiers , 2005, ECSQARU.
[75] Ron Kohavi,et al. Improving simple Bayes , 1997 .
[76] Bernhard Pfahringer,et al. Locally Weighted Naive Bayes , 2002, UAI.
[77] Hong-Bo Shi,et al. Tree-augmented naive Bayes ensembles , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).
[78] Harry Zhang,et al. Learning weighted naive Bayes with accurate ranking , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).
[79] Marvin Minsky,et al. Steps toward Artificial Intelligence , 1995, Proceedings of the IRE.
[80] Dale Schuurmans,et al. Maximum Margin Bayesian Networks , 2005, UAI.
[81] Duc Truong Pham,et al. Building Bayesian network classifiers through a Bayesian complexity monitoring system , 2009 .
[82] Liangxiao Jiang,et al. Lazy Averaged One-Dependence Estimators , 2006, Canadian Conference on AI.
[83] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[84] Mark A. Hall,et al. A decision tree-based attribute weighting filter for naive Bayes , 2006, Knowl. Based Syst..
[85] Michael G. Madden,et al. On the classification performance of TAN and general Bayesian networks , 2008, Knowl. Based Syst..
[86] Timo Koski,et al. Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation , 2006, J. Mach. Learn. Res..
[87] Michael J. Pazzani,et al. Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.
[88] M. E. Maron,et al. On Relevance, Probabilistic Indexing and Information Retrieval , 1960, JACM.
[89] Tom Burr,et al. Causation, Prediction, and Search , 2003, Technometrics.
[90] Anind K. Dey,et al. Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification , 2007, UAI.
[91] 共立出版株式会社. コンピュータ・サイエンス : ACM computing surveys , 1978 .
[92] Duane Szafron,et al. Visual Explanation of Evidence with Additive Classifiers , 2006, AAAI.
[93] Gregory F. Cooper,et al. A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.
[94] Dimitrios Gunopulos,et al. Feature selection for the naive bayesian classifier using decision trees , 2003, Appl. Artif. Intell..
[95] Liangxiao Jiang,et al. A Novel Bayes Model: Hidden Naive Bayes , 2009, IEEE Transactions on Knowledge and Data Engineering.
[96] María S. Pérez-Hernández,et al. Interval Estimation Naïve Bayes , 2003, IDA.
[97] Nir Friedman,et al. Data Analysis with Bayesian Networks: A Bootstrap Approach , 1999, UAI.
[98] Pedro M. Domingos,et al. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.
[99] David Madigan,et al. On the Naive Bayes Model for Text Categorization , 2003, AISTATS.
[100] Pedro Larrañaga,et al. Feature Subset Selection by Bayesian network-based optimization , 2000, Artif. Intell..
[101] María S. Pérez-Hernández,et al. Learning Semi Naïve Bayes Structures by Estimation of Distribution Algorithms , 2003, EPIA.
[102] Franz Pernkopf,et al. Discriminative versus generative parameter and structure learning of Bayesian network classifiers , 2005, ICML.
[103] Tharam S. Dillon,et al. An improved naive Bayesian classifier technique coupled with a novel input solution method [rainfall prediction] , 2001, IEEE Trans. Syst. Man Cybern. Syst..
[104] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[105] Judea Pearl,et al. Equivalence and Synthesis of Causal Models , 1990, UAI.
[106] Henry Tirri,et al. On Discriminative Bayesian Network Classifiers and Logistic Regression , 2005, Machine Learning.
[107] Ron Kohavi,et al. Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid , 1996, KDD.
[108] Rajat Raina,et al. Classification with Hybrid Generative/Discriminative Models , 2003, NIPS.
[109] Russell Greiner,et al. Learning Bayesian Belief Network Classifiers: Algorithms and System , 2001, Canadian Conference on AI.
[110] David Maxwell Chickering,et al. A Transformational Characterization of Equivalent Bayesian Network Structures , 1995, UAI.
[111] Gregory M. Provan,et al. Learning Bayesian Networks Using Feature Selection , 1995, AISTATS.
[112] Bojan Cestnik,et al. Estimating Probabilities: A Crucial Task in Machine Learning , 1990, ECAI.
[113] Mieczyslaw A. Klopotek,et al. Very large Bayesian multinets for text classification , 2005, Future Gener. Comput. Syst..
[114] Dimitris Margaritis,et al. Speculative Markov blanket discovery for optimal feature selection , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).
[115] Duncan Fyfe Gillies,et al. Using Hidden Nodes in Bayesian Networks , 1996, Artif. Intell..
[116] Ana M. Martínez,et al. Supervised Classification with Bayesian Networks: A Review on Models and Applications , 2012 .
[117] Pedro Larrañaga,et al. Filter versus wrapper gene selection approaches in DNA microarray domains , 2004, Artif. Intell. Medicine.
[118] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[119] Daphne Koller,et al. Toward Optimal Feature Selection , 1996, ICML.
[120] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[121] G. Pask,et al. Heuristic Self-Organization in Problems of Engineering Cybernetics , 2003 .
[122] Paola Sebastiani,et al. Robust Bayes classifiers , 2001, Artif. Intell..
[123] Andrés R. Masegosa,et al. A Semi-naive Bayes Classifier with Grouping of Cases , 2007, ECSQARU.
[124] Rema Padman,et al. Tabu Search-Enhanced Graphical Models for Classification in High Dimensions , 2008, INFORMS J. Comput..
[125] Alex Aussem,et al. A novel Markov boundary based feature subset selection algorithm , 2010, Neurocomputing.
[126] María S. Pérez-Hernández,et al. Bayesian network multi-classifiers for protein secondary structure prediction , 2004, Artif. Intell. Medicine.
[127] D. Hand,et al. Idiot's Bayes—Not So Stupid After All? , 2001 .
[128] Henry Tirri,et al. BAYDA: Software for Bayesian Classification and Feature Selection , 1998, KDD.
[129] Ramón López de Mántaras,et al. Robust Bayesian Linear Classifier Ensembles , 2005, ECML.
[130] Shunkai Fu,et al. Local Learning Algorithm for Markov Blanket Discovery , 2007, Australian Conference on Artificial Intelligence.
[131] Eamonn J. Keogh,et al. Learning the Structure of Augmented Bayesian Classifiers , 2002, Int. J. Artif. Intell. Tools.
[132] Thomas D. Nielsen,et al. Latent variable discovery in classification models , 2004, Artif. Intell. Medicine.
[133] Boaz Lerner,et al. Bayesian Class-Matched Multinet Classifier , 2006, SSPR/SPR.
[134] Tao Wang,et al. Generalized Additive Bayesian Network Classifiers , 2007, IJCAI.
[135] Franz Pernkopf,et al. Bayesian network classifiers versus selective k-NN classifier , 2005, Pattern Recognit..
[136] João Gama,et al. Iterative Bayes , 2000, Intell. Data Anal..
[137] Marco Zaffalon. The naive credal classifier , 2002 .
[138] Alan Agresti,et al. Categorical Data Analysis , 2003 .
[139] Nada Lavrac,et al. The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains , 1986, AAAI.
[140] Franz Pernkopf,et al. On Discriminative Parameter Learning of Bayesian Network Classifiers , 2009, ECML/PKDD.
[141] Judea Pearl,et al. Probabilistic reasoning in intelligent systems , 1988 .
[142] Teemu Roos,et al. Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood , 2011, J. Mach. Learn. Res..
[143] Geoffrey I. Webb,et al. To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators , 2007, IEEE Transactions on Knowledge and Data Engineering.
[144] Joaquín Abellán. Application of uncertainty measures on credal sets on the naive Bayesian classifier , 2006, Int. J. Gen. Syst..
[145] Pat Langley,et al. Induction of Selective Bayesian Classifiers , 1994, UAI.
[146] Silja Renooij,et al. Evidence and Scenario Sensitivities in Naive Bayesian Classifiers , 2006, Probabilistic Graphical Models.
[147] Marcel Worring,et al. Face detection by aggregated Bayesian network classifiers , 2001, Pattern Recognit. Lett..
[148] Constantin F. Aliferis,et al. Time and sample efficient discovery of Markov blankets and direct causal relations , 2003, KDD '03.
[149] William J. McGill. Multivariate information transmission , 1954, Trans. IRE Prof. Group Inf. Theory.
[150] Pedro M. Domingos,et al. Learning Bayesian network classifiers by maximizing conditional likelihood , 2004, ICML.
[151] Gregory F. Cooper,et al. A Bayesian Network Classifier that Combines a Finite Mixture Model and a NaIve Bayes Model , 1999, UAI.
[152] Jin Tian,et al. A Hybrid Generative/Discriminative Bayesian Classifier , 2006, FLAIRS Conference.
[153] David Maxwell Chickering,et al. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.
[154] Russell Greiner,et al. Budgeted learning of nailve-bayes classifiers , 2002, UAI 2002.
[155] Peter J. Cameron,et al. Rank three permutation groups with rank three subconstituents , 1985, J. Comb. Theory, Ser. B.
[156] Vladimir Pavlovic,et al. Boosted Bayesian network classifiers , 2008, Machine Learning.
[157] David Maxwell Chickering,et al. Large-Sample Learning of Bayesian Networks is NP-Hard , 2002, J. Mach. Learn. Res..
[158] Ramón López de Mántaras,et al. TAN Classifiers Based on Decomposable Distributions , 2005, Machine Learning.
[159] Nir Friedman,et al. Bayesian Network Classifiers , 1997, Machine Learning.
[160] Olivier François,et al. Learning the Tree Augmented Naive Bayes Classifier from incomplete datasets , 2006, Probabilistic Graphical Models.
[161] Pedro Larrañaga,et al. A review of feature selection techniques in bioinformatics , 2007, Bioinform..
[162] Stan Matwin,et al. Discriminative parameter learning for Bayesian networks , 2008, ICML '08.
[163] Jesper Tegnér,et al. Towards scalable and data efficient learning of Markov boundaries , 2007, Int. J. Approx. Reason..
[164] Kazuo J. Ezawa,et al. Constructing Bayesian Networks to Predict Uncollectible Telecommunications Accounts , 1996, IEEE Expert.
[165] Geoffrey I. Webb,et al. Adjusted Probability Naive Bayesian Induction , 1998, Australian Joint Conference on Artificial Intelligence.
[166] Igor Kononenko,et al. Successive Naive Bayesian Classifier , 1993, Informatica.
[167] M. Pazzani. Constructive Induction of Cartesian Product Attributes , 1998 .
[168] Liangxiao Jiang,et al. Improving Tree augmented Naive Bayes for class probability estimation , 2012, Knowl. Based Syst..
[169] Jeff A. Bilmes,et al. Dynamic Bayesian Multinets , 2000, UAI.
[170] J. Kruskal. On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .
[171] Gregory F. Cooper,et al. Exact model averaging with naive Bayesian classifiers , 2002, ICML.
[172] Anderson Ara,et al. Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool , 2012, Expert Syst. Appl..
[173] Ricardo Vilalta,et al. A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes , 2003, ECML.
[174] D. M. Titterington,et al. Joint discriminative-generative modelling based on statistical tests for classification , 2010, Pattern Recognit. Lett..
[175] Wray L. Buntine. Theory Refinement on Bayesian Networks , 1991, UAI.
[176] David G. Stork,et al. Pattern Classification , 1973 .
[177] Thomas D. Nielsen,et al. Classification using Hierarchical Naïve Bayes models , 2006, Machine Learning.
[178] Azuraliza Abu Bakar,et al. Naïve bayes variants in classification learning , 2010, 2010 International Conference on Information Retrieval & Knowledge Management (CAMP).
[179] Pat Langley,et al. Induction of Recursive Bayesian Classifiers , 1993, ECML.
[180] Marco Wiering,et al. Feature selection for Bayesian network classifiers using the MDL-FS score , 2010, Int. J. Approx. Reason..
[181] S. Lauritzen,et al. The TM algorithm for maximising a conditional likelihood function , 2001 .
[182] Naonori Ueda,et al. A hybrid generative/discriminative approach to text classification with additional information , 2007, Inf. Process. Manag..