Naïve Bayesian Classifiers with Multinomial Models for rRNA Taxonomic Assignment

2013 ◽  
Vol 10 (5) ◽  
pp. 1-1 ◽  
Author(s):  
Kuan-Liang Liu ◽  
Tzu-Tsung Wong
Keyword(s):  
Bayesian Classifiers ◽  
Taxonomic Assignment ◽  
Naive Bayesian ◽  
Multinomial Models ◽  
Naïve Bayesian

On the Use of Naive Bayesian Classifiers for Detecting Elementary and Coordinated Attacks

2010 ◽  
Vol 105 (4) ◽  
pp. 435-466 ◽  
Author(s):  
Tayeb Kenaza ◽  
Karim Tabia ◽  
Salem Benferhat
Keyword(s):  
Bayesian Classifiers ◽  
Naive Bayesian ◽  
Naïve Bayesian

Publishing naive Bayesian classifiers

2009 ◽  
Vol 2 (1) ◽  
pp. 1174-1185 ◽  
Author(s):  
Barzan Mozafari ◽  
Carlo Zaniolo
Keyword(s):  
Bayesian Classifiers ◽  
Naive Bayesian ◽  
Naïve Bayesian

Correlation Analysis in Classifiers

2010 ◽  
pp. 204-218
Author(s):  
Vincent Lemaire ◽  
Carine Hue ◽  
Olivier Bernier
Keyword(s):  
Correlation Analysis ◽  
General Framework ◽  
New Method ◽  
Classification Model ◽  
Data Sets ◽  
Bayesian Classifiers ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Predictive Probability ◽  
Input Variables

This chapter presents a new method to analyze the link between the probabilities produced by a classification model and the variation of its input values. The goal is to increase the predictive probability of a given class by exploring the possible values of the input variables taken independently. The proposed method is presented in a general framework, and then detailed for naive Bayesian classifiers. We also demonstrate the importance of “lever variables”, variables which can conceivably be acted upon to obtain specific results as represented by class probabilities, and consequently can be the target of specific policies. The application of the proposed method to several data sets shows that such an approach can lead to useful indicators.

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