Abstract
Feature selection plays an important role in text categorization. Many sophisticated feature selection methods such as Information Gain (IG), Mutual Information (MI) and χ2 statistic measure (CHI) have been proposed. However, when compared to these above methods, a very simple technique called Document Frequency thresholding (DF) has shown to be one of the best methods either on Chinese or English text data. A problem is that DF method is usually considered as an empirical approach and it does not consider Term Frequency (TF) factor. In this paper, we put forward an extended DF method called TFDF which combines the Term Frequency (TF) factor. Experimental results on Reuters-21578 and OHSUMED corpora show that TFDF performs much better than the original DF method.
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Xu, Y., Wang, B., Li, J., Jing, H. (2008). An Extended Document Frequency Metric for Feature Selection in Text Categorization. In: Li, H., Liu, T., Ma, WY., Sakai, T., Wong, KF., Zhou, G. (eds) Information Retrieval Technology. AIRS 2008. Lecture Notes in Computer Science, vol 4993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68636-1_8
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DOI: https://doi.org/10.1007/978-3-540-68636-1_8
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