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An analysis of Bayesian classifiers
 IN PROCEEDINGS OF THE TENTH NATIONAL CONFERENCE ON ARTI CIAL INTELLIGENCE
, 1992
"... In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability that t ..."
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Cited by 440 (17 self)
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In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability
Induction of Selective Bayesian Classifiers
 CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
, 1994
"... In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that carries out a greedy search through the space ..."
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Cited by 265 (7 self)
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In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that carries out a greedy search through the space
On the optimality of the simple Bayesian classifier under zeroone loss
 MACHINE LEARNING
, 1997
"... The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containin ..."
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Cited by 818 (27 self)
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The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains
Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier
"... The simple Bayesian classifier (SBC) is commonly thought to assume that attributes are independent given the class, but this is apparently contradicted by the surprisingly good performance it exhibits in many domains that contain clear attribute dependences. No explanation for this has been proposed ..."
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Cited by 361 (8 self)
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The simple Bayesian classifier (SBC) is commonly thought to assume that attributes are independent given the class, but this is apparently contradicted by the surprisingly good performance it exhibits in many domains that contain clear attribute dependences. No explanation for this has been
Searching for Dependencies in Bayesian Classifiers
, 1996
"... Naive Bayesian classifiers which make independence assumptions perform remarkably well on some data sets but poorly on others. We explore ways to improve the Bayesian classifier by searching for dependencies among attributes. We propose and evaluate two algorithms for detecting dependencies among at ..."
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Cited by 74 (5 self)
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Naive Bayesian classifiers which make independence assumptions perform remarkably well on some data sets but poorly on others. We explore ways to improve the Bayesian classifier by searching for dependencies among attributes. We propose and evaluate two algorithms for detecting dependencies among
Induction of Recursive Bayesian Classifiers
 In Brazdil P.B.(ed.), Machine Learning: ECML93
, 1993
"... Abstract. In this paper, we review the induction of simple Bayesian classifiers, note some of their drawbacks, and describe a recursive algorithm that constructs a hierarchy of probabilistic concept descriptions. We posit that this approach should outperform the simpler scheme in domains that involv ..."
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Cited by 50 (3 self)
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Abstract. In this paper, we review the induction of simple Bayesian classifiers, note some of their drawbacks, and describe a recursive algorithm that constructs a hierarchy of probabilistic concept descriptions. We posit that this approach should outperform the simpler scheme in domains
Naive Bayesian Classifier Committees
 Proceedings of the 10th European Conference on Machine Learning
, 1998
"... . The naive Bayesian classifier provides a very simple yet surprisingly accurate technique for machine learning. Some researchers have examined extensions to the naive Bayesian classifier that seek to further improve the accuracy. For example, a naive Bayesian tree approach generates a decision tree ..."
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Cited by 15 (1 self)
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. The naive Bayesian classifier provides a very simple yet surprisingly accurate technique for machine learning. Some researchers have examined extensions to the naive Bayesian classifier that seek to further improve the accuracy. For example, a naive Bayesian tree approach generates a decision
Visualizing the Simple Bayesian Classifier
, 1997
"... The simple Bayesian classifier (SBC), sometimes called NaiveBayes, is built based on a conditional independence model of each attribute given the class. The model was previously shown to be surprisingly robust to obvious violations of this independence assumption, yielding accurate classification m ..."
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Cited by 48 (13 self)
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The simple Bayesian classifier (SBC), sometimes called NaiveBayes, is built based on a conditional independence model of each attribute given the class. The model was previously shown to be surprisingly robust to obvious violations of this independence assumption, yielding accurate classification
Simple Estimators for Relational Bayesian Classifiers
 In Proceedings of the 3rd IEEE International Conference on Data Mining
, 2003
"... This paper evaluates several modifications of the Simple Bayesian Classifier to enable estimation and inference over relational data. The resulting Relational Bayesian Classifiers are evaluated on three realworld datasets and compared to a baseline SBC using no relational information ..."
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Cited by 76 (20 self)
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This paper evaluates several modifications of the Simple Bayesian Classifier to enable estimation and inference over relational data. The resulting Relational Bayesian Classifiers are evaluated on three realworld datasets and compared to a baseline SBC using no relational information
Results 1  10
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