Robustness of regularized linear classification methods in text categorization (2003) [11 citations — 3 self]

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by Jian Zhang, Yiming Yang

In Proceedings of SIGIR 2003: The Twenty-Sixth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

http://www.cs.cmu.edu/~yiming/papers.yy/jzhang-sigir03.ps.gz

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@INPROCEEDINGS{Zhang03robustnessof,
    author = {Jian Zhang and Yiming Yang},
    title = {Robustness of regularized linear classification methods in text categorization},
    booktitle = {In Proceedings of SIGIR 2003: The Twenty-Sixth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
    year = {2003},
    pages = {190--197}
}

Abstract:

Real-world applications often require the classication of documents under situations of small number of features, mis-labeled documents and rare positive examples. This paper investigates the robustness of three regularized linear classi cation methods (SVM, ridge regression and logistic regression) under above situations. We compare these methods in terms of their loss functions and score distributions, and establish the connection between their optimization problems and generalization error bounds. Several sets of controlled experiments on the Reuters-21578 corpus are conducted to investigate the robustness of these methods. Our results show that ridge regression seems to be the most promising candidate for rare class problems.

Citations

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