Combining Modifications to Multinomial Naive Bayes for Text Classification

2012 ◽  
pp. 114-125
Author(s):  
Antti Puurula
Keyword(s):  
Text Classification ◽  
Naive Bayes ◽  
Naïve Bayes

An Improved FloatBoost Algorithm for Naïve Bayes Text Classification

2005 ◽  
pp. 162-171 ◽  
Author(s):  
Xiaoming Liu ◽  
Jianwei Yin ◽  
Jinxiang Dong ◽  
Memon Abdul Ghafoor
Keyword(s):  
Text Classification ◽  
Naive Bayes ◽  
Naïve Bayes

Arabic Text Classification using K-NN and Naive Bayes

2008 ◽  
Vol 4 (7) ◽  
pp. 600-605 ◽  
Author(s):  
Mohammed J. Bawaneh ◽  
Mahmud S. Alkoffash ◽  
Adnan I. Al Rabea
Keyword(s):  
Text Classification ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Arabic Text ◽  
Arabic Text Classification

A Study of Intelligent Recommendation System based on Naive Bayes Text Classification and Collaborative Filtering

2010 ◽  
Vol 41 (4) ◽  
pp. 227-249 ◽  
Author(s):  
Sang-Gi Lee ◽  
Byeong-Seop Lee ◽  
Byeong-Yong Bak ◽  
Hye-Kyong Hwang
Keyword(s):  
Collaborative Filtering ◽  
Text Classification ◽  
Recommendation System ◽  
Naive Bayes ◽  
Naïve Bayes

Improving Techniques for Naïve Bayes Text Classifiers

2010 ◽  
pp. 111-127
Author(s):  
Han-joon Kim
Keyword(s):  
Text Classification ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Systems ◽  
Classification Model ◽  
Learning Approaches ◽  
Learning Framework ◽  
The Em Algorithm ◽  
Meta Learning ◽  
Text Classifiers

This chapter introduces two practical techniques for improving Naïve Bayes text classifiers that are widely used for text classification. The Naïve Bayes has been evaluated to be a practical text classification algorithm due to its simple classification model, reasonable classification accuracy, and easy update of classification model. Thus, many researchers have a strong incentive to improve the Naïve Bayes by combining it with other meta-learning approaches such as EM (Expectation Maximization) and Boosting. The EM approach is to combine the Naïve Bayes with the EM algorithm and the Boosting approach is to use the Naïve Bayes as a base classifier in the AdaBoost algorithm. For both approaches, a special uncertainty measure fit for Naïve Bayes learning is used. In the Naïve Bayes learning framework, these approaches are expected to be practical solutions to the problem of lack of training documents in text classification systems.

scholarly journals Poisson naive Bayes for text classification with feature weighting

2003 ◽  
Author(s):  
Sang-Bum Kim ◽  
Hee-Cheol Seo ◽  
Hae-Chang Rim
Keyword(s):  
Text Classification ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Feature Weighting
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