Automatically assigned DDC number: 006312

Manually assigned DDC number: 006312

Number of references: 16

Title: A Re-Examination of Text Categorization Methods

Author:

Author:

Subject: Yiming Yang,Xin Liu A Re-Examination of Text Categorization Methods

Description: This paper reports a controlled study with statistical significance tests on 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 on 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).

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 2000-04-29

Pubyear: 1999

Format: ps

Identifier: http://citeseer.ist.psu.edu/361822.html

Source: http://www.cs.cmu.edu/~yiming/papers.yy/sigir99.ps

Language: en

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