Document (#28387)

Author

Yang, Y.
Liu, X.

Title

¬A re-examination of text categorization methods

Source

http://www.cs.rit.edu/~dmrg/dm_winter/ reading/re_examTCMethods.pdf

Year

1999

Abstract

This paper reports a controlled study with statistical significance tests an five text categorization methods: the Support Vector Machines (SVM), a k-Nearest Neighbor (kNN) classifier, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a Naive Bayes (NB) classifier. We focus an the robustness of these methods in dealing with a skewed category distribution, and their performance as function of the training-set category frequency. Our results show that SVM, kNN and LLSF significantly outperform NNet and NB when the number of positive training instances per category are small (less than ten, and that all the methods perform comparably when the categories are sufficiently common (over 300 instances).

Content

Beitrag zu: 22nd Annual International SIGIR

Theme

Automatisches Klassifizieren

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