DocumentCode :
2388193
Title :
Naïve Bayes Text Classifier
Author :
Zhang, Haiyi ; Li, Di
Author_Institution :
Acadia Univ., Wolfville
fYear :
2007
fDate :
2-4 Nov. 2007
Firstpage :
708
Lastpage :
708
Abstract :
Text classification algorithms, such SVM, and Naive Bayes, have been developed to build up search engines and construct spam email filters. As a simple yet powerful sample of Bayesian theorem, naive Bayes shows advantages in text classification yielding satisfactory results. In this paper, a spam email detector is developed using naive Bayes algorithm. We use pre-classified emails (priory knowledge) to train the spam email detector. With the model generated from the training step, the detector is able to decide whether an email is a spam email or an ordinary email.
Keywords :
Bayes methods; text analysis; unsolicited e-mail; Bayesian theorem; naive Bayes; search engines; spam email filters; text classification algorithms; Bayesian methods; Classification algorithms; Computer science; Detectors; Inference algorithms; Probability; Search engines; Support vector machine classification; Support vector machines; Text categorization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Granular Computing, 2007. GRC 2007. IEEE International Conference on
Conference_Location :
Fremont, CA
Print_ISBN :
978-0-7695-3032-1
Type :
conf
DOI :
10.1109/GrC.2007.40
Filename :
4403192
Link To Document :
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