Data mining tasks and methods: Classification: Bayesian classification

Bayesian classification addresses the classification problem by learning the distribution of instances given different class values. We review the basic notion of Bayesian classification, describe in some detail the naive Bayesian classifier, and briefly discuss some extensions.

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

[2]  Ron Kohavi,et al.  Improving simple Bayes , 1997 .

[3]  Igor Kononenko,et al.  Semi-Naive Bayesian Classifier , 1991, EWSL.

[4]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[5]  Pat Langley,et al.  Induction of Selective Bayesian Classifiers , 1994, UAI.

[6]  D. Rubin.  INFERENCE AND MISSING DATA , 1975 .

[7]  Jerome H. Friedman,et al.  On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality , 2004, Data Mining and Knowledge Discovery.

[8]  Ron Kohavi,et al.  Data Mining Using MLC a Machine Learning Library in C++ , 1996, Int. J. Artif. Intell. Tools.

[9]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[10]  Michael J. Pazzani,et al.  Searching for Dependencies in Bayesian Classifiers , 1995, AISTATS.

[11]  Sean R. Eddy,et al.  Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998 .

[12]  Bojan Cestnik,et al.  Estimating Probabilities: A Crucial Task in Machine Learning , 1990, ECAI.

[13]  Pat Langley,et al.  An Analysis of Bayesian Classifiers , 1992, AAAI.

[14]  Kazuo J. Ezawa,et al.  Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures , 1995, UAI.

[15]  Ron Kohavi,et al.  Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology , 1995, KDD.

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

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

[18]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[19]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

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

[21]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

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

[23]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[24]  Ron Kohavi,et al.  MineSet: An Integrated System for Data Mining , 1997, KDD.

[25]  Igor Kononenko,et al.  Inductive and Bayesian learning in medical diagnosis , 1993, Appl. Artif. Intell..

[26]  Peter Clark,et al.  The CN2 Induction Algorithm , 1989, Machine Learning.