Multi-dimensional classification with Bayesian networks

[1]  International Journal of Approximate Reasoning , 2012, SOCO 2012.

[2]  Grigorios Tsoumakas,et al.  Random K-labelsets for Multilabel Classification , 2022 .

[3]  Marco Wiering,et al.  Feature selection for Bayesian network classifiers using the MDL-FS score , 2010, Int. J. Approx. Reason..

[4]  Eyke Hüllermeier,et al.  On label dependence in multilabel classification , 2010, ICML 2010.

[5]  K. Dembczynski,et al.  On Label Dependence in Multi-Label Classification , 2010 .

[6]  Grigorios Tsoumakas,et al.  Mining Multi-label Data , 2010, Data Mining and Knowledge Discovery Handbook.

[7]  Yuh-Jye Lee,et al.  Periodic step-size adaptation in second-order gradient descent for single-pass on-line structured learning , 2009, Machine Learning.

[8]  Yasemin Altun,et al.  Guest editorial: special issue on structured prediction , 2009, Machine Learning.

[9]  Christoph H. Lampert,et al.  Structured prediction by joint kernel support estimation , 2009, Machine Learning.

[10]  Andrew McCallum,et al.  Piecewise training for structured prediction , 2009, Machine Learning.

[11]  Yi Mao,et al.  Generalized isotonic conditional random fields , 2009, Machine Learning.

[12]  Rina Dechter,et al.  AND/OR Branch-and-Bound search for combinatorial optimization in graphical models , 2009, Artif. Intell..

[13]  HüllermeierEyke,et al.  Combining instance-based learning and logistic regression for multilabel classification , 2009 .

[14]  Alex Alves Freitas,et al.  A Tutorial on Multi-label Classification Techniques , 2009, Foundations of Computational Intelligence.

[15]  Eyke Hüllermeier,et al.  Combining instance-based learning and logistic regression for multilabel classification , 2009, Machine Learning.

[16]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[17]  Pedro Larrañaga,et al.  Bayesian classifiers based on kernel density estimation: Flexible classifiers , 2009, Int. J. Approx. Reason..

[18]  E. Hüllermeier,et al.  A Simple Instance-Based Approach to Multilabel Classification Using the Mallows Model , 2009 .

[19]  Grigorios Tsoumakas,et al.  Correlation-Based Pruning of Stacked Binary Relevance Models for Multi-Label Learning , 2009 .

[20]  Geoff Holmes,et al.  Multi-label Classification Using Ensembles of Pruned Sets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[21]  Saso Dzeroski,et al.  Decision trees for hierarchical multi-label classification , 2008, Machine Learning.

[22]  Eyke Hüllermeier,et al.  Label ranking by learning pairwise preferences , 2008, Artif. Intell..

[23]  José Antonio Lozano,et al.  Multi-Objective Learning of Multi-Dimensional Bayesian Classifiers , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

[24]  Wei-Pang Yang,et al.  A discretization algorithm based on Class-Attribute Contingency Coefficient , 2008, Inf. Sci..

[25]  Grigorios Tsoumakas,et al.  Effective and Efficient Multilabel Classification in Domains with Large Number of Labels , 2008 .

[26]  Grigorios Tsoumakas,et al.  Multi-Label Classification of Music into Emotions , 2008, ISMIR.

[27]  S. García,et al.  An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons , 2008 .

[28]  Linda C. van der Gaag,et al.  Inference and Learning in Multi-dimensional Bayesian Network Classifiers , 2007, ECSQARU.

[29]  Thomas Hofmann,et al.  Predicting Structured Data (Neural Information Processing) , 2007 .

[30]  Gökhan BakIr,et al.  Predicting Structured Data , 2008 .

[31]  Zhi-Hua Zhou,et al.  ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..

[32]  Grigorios Tsoumakas,et al.  Multi-Label Classification: An Overview , 2007, Int. J. Data Warehous. Min..

[33]  Delcroix Véronique,et al.  BAYESIAN NETWORKS VERSUS OTHER PROBABILISTIC MODELS FOR THE MULTIPLE DIAGNOSIS OF LARGE DEVICES , 2007 .

[34]  McCallumAndrew,et al.  Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data , 2007 .

[35]  Glenn Fung,et al.  Automated Heart Wall Motion Abnormality Detection from Ultrasound Images Using Bayesian Networks , 2007, IJCAI.

[36]  Andrew McCallum,et al.  Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data , 2004, J. Mach. Learn. Res..

[37]  Sylvain Piechowiak,et al.  Bayesian Networks versus Other Probabilistic Models for the Multiple Diagnosis of Large Devices , 2007, Int. J. Artif. Intell. Tools.

[38]  Silja Renooij,et al.  Evidence and Scenario Sensitivities in Naive Bayesian Classifiers , 2006, Probabilistic Graphical Models.

[39]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[40]  Zhi-Hua Zhou,et al.  Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization , 2006, IEEE Transactions on Knowledge and Data Engineering.

[41]  Linda C. van der Gaag,et al.  Multi-dimensional Bayesian Network Classifiers , 2006, Probabilistic Graphical Models.

[42]  Thomas Hofmann,et al.  Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..

[43]  Pedro Larrañaga,et al.  Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS , 2005, J. Biomed. Informatics.

[44]  Daniel Marcu,et al.  Learning as search optimization: approximate large margin methods for structured prediction , 2005, ICML.

[45]  Thomas Stützle,et al.  Efficient Stochastic Local Search for MPE Solving , 2005, IJCAI.

[46]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[47]  A. Darwiche,et al.  Complexity Results and Approximation Strategies for MAP Explanations , 2011, J. Artif. Intell. Res..

[48]  Jiebo Luo,et al.  Learning multi-label scene classification , 2004, Pattern Recognit..

[49]  Sunita Sarawagi,et al.  Discriminative Methods for Multi-labeled Classification , 2004, PAKDD.

[50]  Yoram Singer,et al.  BoosTexter: A Boosting-based System for Text Categorization , 2000, Machine Learning.

[51]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[52]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[53]  Ben Taskar,et al.  Max-Margin Markov Networks , 2003, NIPS.

[54]  O. Kipersztok,et al.  Evidence-based Bayesian networks approach to airplane maintenance , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[55]  Naonori Ueda,et al.  Parametric Mixture Models for Multi-Labeled Text , 2002, NIPS.

[56]  F. V. Jensen,et al.  The SACSO methodology for troubleshooting complex systems , 2001, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[57]  Peter J. F. Lucas Bayesian model-based diagnosis , 2001, Int. J. Approx. Reason..

[58]  Rina Dechter,et al.  A general scheme for automatic generation of search heuristics from specification dependencies , 2001, Artif. Intell..

[59]  Jason Weston,et al.  A kernel method for multi-labelled classification , 2001, NIPS.

[60]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[61]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[62]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[63]  Luis Enrique Sucar,et al.  Probabilistic Model-Based Diagnosis , 2000, MICAI.

[64]  Rina Dechter,et al.  Bucket Elimination: A Unifying Framework for Reasoning , 1999, Artif. Intell..

[65]  Rina Dechter,et al.  Mini-Bucket Heuristics for Improved Search , 1999, UAI.

[66]  Pat Langley,et al.  Tractable Average-Case Analysis of Naive Bayesian Classifiers , 1999, ICML.

[67]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

[68]  Ashraf M. Abdelbar,et al.  Approximating MAPs for Belief Networks is NP-Hard and Other Theorems , 1998, Artif. Intell..

[69]  D.-J. Guan,et al.  GENERALIZED GRAY CODES WITH APPLICATIONS , 1998 .

[70]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[71]  Rina Dechter,et al.  A Scheme for Approximating Probabilistic Inference , 1997, UAI.

[72]  Mehran Sahami,et al.  Learning Limited Dependence Bayesian Classifiers , 1996, KDD.

[73]  J. Breese,et al.  Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment , 1996, UAI.

[74]  Adnan Darwiche Model-Based Diagnosis using Causal Networks , 1995, IJCAI.

[75]  Edzard S. Gelsema,et al.  Abductive reasoning in Bayesian belief networks using a genetic algorithm , 1995, Pattern Recognit. Lett..

[76]  Solomon Eyal Shimony,et al.  Finding MAPs for Belief Networks is NP-Hard , 1994, Artif. Intell..

[77]  David J. Spiegelhalter,et al.  Bayesian analysis in expert systems , 1993 .

[78]  Mark A. Kramer,et al.  GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks , 1993, UAI.

[79]  Zhaoyu Li,et al.  An efficient approach for finding the MPE in belief networks , 1993, UAI.

[80]  A. P. Dawid,et al.  Applications of a general propagation algorithm for probabilistic expert systems , 1992 .

[81]  Eugene Santos,et al.  On the Generation of Alternative Explanations with Implications for Belief Revision , 1991, UAI.

[82]  Solomon Eyal Shimony,et al.  A new algorithm for finding MAP assignments to belief networks , 1990, UAI.

[83]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[84]  Hector Geffner,et al.  An Improved Constraint-Propagation Algorithm for Diagnosis , 1987, IJCAI.

[85]  Yun Peng,et al.  A Probabilistic Causal Model for Diagnostic Problem Solving Part II: Diagnostic Strategy , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[86]  Yun Peng,et al.  A Probabilistic Causal Model for Diagnostic Problem Solving Part I: Integrating Symbolic Causal Inference with Numeric Probabilistic Inference , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[87]  Max Henrion,et al.  Propagating uncertainty in bayesian networks by probabilistic logic sampling , 1986, UAI.

[88]  M. Stone Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[89]  David G. Stork,et al.  Pattern Classification , 1973 .

[90]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[91]  Marvin Minsky,et al.  Steps toward Artificial Intelligence , 1995, Proceedings of the IRE.