Abstract
In this paper, we propose a probabilistic approach to feature selection for multi-class text categorization. Specifically, we regard document class and occurrence of each feature as events, calculate the probability of occurrence of each feature by the theorem on the total probability and utilize the values as a ranking criterion. Experiments on Reuters-2000 collection show that the proposed method can yield better performance than information gain and χ-square, which are two well-known feature selection methods.
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Wu, K., Lu, BL., Uchiyama, M., Isahara, H. (2007). A Probabilistic Approach to Feature Selection for Multi-class Text Categorization. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_153
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DOI: https://doi.org/10.1007/978-3-540-72383-7_153
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72382-0
Online ISBN: 978-3-540-72383-7
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