Add to ORKGWe investigate a simple semi-naive Bayesian ranking method that combines naive Bayes with induction of decision tables. Naive Bayes and decision tables can both be trained effi-ciently, and the same holds true for the combined semi-naive model. We show that the resulting ranker, compared to ei-ther component technique, frequently significantly increases AUC. For some datasets it significantly improves on both techniques. This is also the case when attribute selection is performed in naive Bayes and its semi-naive variant
The naive Bayes classifier continues to be a popular learning algorithm for data mining applications...
. Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with s...
International audienceDue to its linear complexity, naive Bayes classification remains an attractive...
We investigate a simple semi-naive Bayesian ranking method that combine naive Bayes with induction o...
We investigate a simple semi-naive Bayesian ranking method that combine naive Bayes with induction o...
We investigate a simple semi-naive Bayesian ranking method that combine naive Bayes with induction o...
Tree augmented naive Bayes is a semi-naive Bayesian Learning method. It relaxes the naive Bayes attr...
The naive Bayes is a competitive classifier that makes strong conditional independence assumptions. ...
The naive Bayes classifier continues to be a popular learning algorithm for data mining applications...
Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classi-ca...
LBR is a lazy semi-naive Bayesian classifier learning technique, designed to alleviate the attribute...
Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in ...
BayesClass implements ten algorithms for learning Bayesian network classifiers from discrete data. T...
Despite its simplicity, the naïve Bayes learning scheme performs well on most classification tasks, ...
. This paper investigates boosting naive Bayesian classification. It first shows that boosting canno...
The naive Bayes classifier continues to be a popular learning algorithm for data mining applications...
. Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with s...
International audienceDue to its linear complexity, naive Bayes classification remains an attractive...
We investigate a simple semi-naive Bayesian ranking method that combine naive Bayes with induction o...
We investigate a simple semi-naive Bayesian ranking method that combine naive Bayes with induction o...
We investigate a simple semi-naive Bayesian ranking method that combine naive Bayes with induction o...
Tree augmented naive Bayes is a semi-naive Bayesian Learning method. It relaxes the naive Bayes attr...
The naive Bayes is a competitive classifier that makes strong conditional independence assumptions. ...
The naive Bayes classifier continues to be a popular learning algorithm for data mining applications...
Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classi-ca...
LBR is a lazy semi-naive Bayesian classifier learning technique, designed to alleviate the attribute...
Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in ...
BayesClass implements ten algorithms for learning Bayesian network classifiers from discrete data. T...
Despite its simplicity, the naïve Bayes learning scheme performs well on most classification tasks, ...
. This paper investigates boosting naive Bayesian classification. It first shows that boosting canno...
The naive Bayes classifier continues to be a popular learning algorithm for data mining applications...
. Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with s...
International audienceDue to its linear complexity, naive Bayes classification remains an attractive...