期刊
JOURNAL OF INFORMATION SCIENCE
卷 44, 期 1, 页码 48-59出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0165551516677946
关键词
Bayesian Naive Bayes classifier; event model; Naive Bayes classifier; text classification
资金
- National Science Foundation of China [71403255]
- Key Technologies R&D Program of Chinese 12th Five-Year Plan [2015BAH25F01]
Text classification is the task of assigning predefined categories to natural language documents, and it can provide conceptual views of document collections. The Naive Bayes (NB) classifier is a family of simple probabilistic classifiers based on a common assumption that all features are independent of each other, given the category variable, and it is often used as the baseline in text classification. However, classical NB classifiers with multinomial, Bernoulli and Gaussian event models are not fully Bayesian. This study proposes three Bayesian counterparts, where it turns out that classical NB classifier with Bernoulli event model is equivalent to Bayesian counterpart. Finally, experimental results on 20 newsgroups and WebKB data sets show that the performance of Bayesian NB classifier with multinomial event model is similar to that of classical counterpart, but Bayesian NB classifier with Gaussian event model is obviously better than classical counterpart.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据