Class binarizations are effective methods that break multi-class problem down into several 2- class or binary problems to improve weak learners. This paper analyzes which effects these methods have if we choose a Naive Bayes learner for the base classifier. We consider the known unordered and pairwise class binarizations and propose an alternative approach for a pairwise calculation of a modified Naive Bayes classifier
Naive Bayes classifiers tend to perform very well on a large number of problem domains, although th...
Tree augmented naive Bayes is a semi-naive Bayesian Learning method. It relaxes the naive Bayes attr...
BayesClass implements ten algorithms for learning Bayesian network classifiers from discrete data. T...
Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in ...
Abstract. We investigate why discretization can be effective in naive-Bayes learning. We prove a the...
The naive Bayes is a competitive classifier that makes strong conditional independence assumptions. ...
The two most well-known approaches for reducing a multiclass classification problem to a set of bina...
Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in ...
AbstractNaive Bayes is a well-known and studied algorithm both in statistics and machine learning. B...
AbstractThe use of Bayesian Networks (BNs) as classifiers in different fields of application has rec...
The naïve Bayes classifier is a simple form of Bayesian classifiers which assumes all the features a...
Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classi-ca...
. Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with s...
Abstract. This paper argues that two commonly-used discretization approaches, fixed k-interval discr...
Binary decomposition methods transform multiclass learning problems into a series of two-class learn...
Naive Bayes classifiers tend to perform very well on a large number of problem domains, although th...
Tree augmented naive Bayes is a semi-naive Bayesian Learning method. It relaxes the naive Bayes attr...
BayesClass implements ten algorithms for learning Bayesian network classifiers from discrete data. T...
Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in ...
Abstract. We investigate why discretization can be effective in naive-Bayes learning. We prove a the...
The naive Bayes is a competitive classifier that makes strong conditional independence assumptions. ...
The two most well-known approaches for reducing a multiclass classification problem to a set of bina...
Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in ...
AbstractNaive Bayes is a well-known and studied algorithm both in statistics and machine learning. B...
AbstractThe use of Bayesian Networks (BNs) as classifiers in different fields of application has rec...
The naïve Bayes classifier is a simple form of Bayesian classifiers which assumes all the features a...
Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classi-ca...
. Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with s...
Abstract. This paper argues that two commonly-used discretization approaches, fixed k-interval discr...
Binary decomposition methods transform multiclass learning problems into a series of two-class learn...
Naive Bayes classifiers tend to perform very well on a large number of problem domains, although th...
Tree augmented naive Bayes is a semi-naive Bayesian Learning method. It relaxes the naive Bayes attr...
BayesClass implements ten algorithms for learning Bayesian network classifiers from discrete data. T...
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