期刊
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
卷 25, 期 2, 页码 273-286出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/0952813X.2012.721010
关键词
text classification; naive Bayes; locally weighted learning; multinomial naive Bayes; complement naive Bayes; the one-versus-all-but-one model
资金
- National Natural Science Foundation of China [60905033, 61203287]
- Provincial Natural Science Foundation of Hubei [2011CDA103]
- Fundamental Research Funds for the Central Universities [CUG110405, CUG090109]
Due to being fast, easy to implement and relatively effective, some state-of-the-art naive Bayes text classifiers with the strong assumption of conditional independence among attributes, such as multinomial naive Bayes, complement naive Bayes and the one-versus-all-but-one model, have received a great deal of attention from researchers in the domain of text classification. In this article, we revisit these naive Bayes text classifiers and empirically compare their classification performance on a large number of widely used text classification benchmark datasets. Then, we propose a locally weighted learning approach to these naive Bayes text classifiers. We call our new approach locally weighted naive Bayes text classifiers (LWNBTC). LWNBTC weakens the attribute conditional independence assumption made by these naive Bayes text classifiers by applying the locally weighted learning approach. The experimental results show that our locally weighted versions significantly outperform these state-of-the-art naive Bayes text classifiers in terms of classification accuracy.
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