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Text classification model of Uyghur based on improved Bayes
Zhou, Xi
Source PublicationJournal of Computational Information Systems

Nai¨ve Bayesian is a simple and efficient pattern recognition algorithm, and has been widely used in text classification. But the assumption of Nai¨ve Bayesian is often not hold in the real application. To improve the performance of the Classifier, a weighted Bayesian method is proposed based on feature selection weight for taking into account different conditions have different effects to the decision conditions. Firstly, represent the effect value of every feature by the combination of the Chi Square value and IDF (Inverse Document Frequency). Then, the weight of every feature is computed by the effect value. Lastly, weighted Bayesian Classifier is built on the weight. By experiments, This method has a better classification performance than Nai¨ve Bayesian Classifier.

Indexed ByEI
Document Type期刊论文
Corresponding AuthorZhou, Xi
AffiliationThe Xinjiang Technical Institute of Physics and Chemistry, Urumqi 830011, China;Graduate University of Chinese Academy of Sciences, Beijing 100190, China
Recommended Citation
GB/T 7714
Zhou, Xi. Text classification model of Uyghur based on improved Bayes[J]. Journal of Computational Information Systems,2013,9(11):4319-4327.
APA Zhou, Xi.(2013).Text classification model of Uyghur based on improved Bayes.Journal of Computational Information Systems,9(11),4319-4327.
MLA Zhou, Xi."Text classification model of Uyghur based on improved Bayes".Journal of Computational Information Systems 9.11(2013):4319-4327.
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