Abstract
Naive Bayes classifier is widely used in text classification tasks, and it can perform surprisingly well, it is often regarded as a baseline. But previous researches show that the skewed distribution of training collection may cause poor results in text classification. This paper presents a new method to deal with this situation. We introduce a conditional probability which takes into account both the information of the whole corpus and each category. Our proposed method performs well in the standard benchmark collections, competing with the state-of-the-art text classifiers especially for the skewed data.
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Jiang, Y., Lin, H., Wang, X., Lu, D. (2011). A Technique for Improving the Performance of Naive Bayes Text Classification. In: Gong, Z., Luo, X., Chen, J., Lei, J., Wang, F.L. (eds) Web Information Systems and Mining. WISM 2011. Lecture Notes in Computer Science, vol 6988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23982-3_25
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DOI: https://doi.org/10.1007/978-3-642-23982-3_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23981-6
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