Yang, Yiming. 2001. A study on thresholding strategies for text categorization. In W. Bruce Croft, David J. Harper, Donald H. Kraft, and Justin Zobel, editors, Proceedings of SIGIR-01, 24th ACM International Conference on Research and Development in Information Retrieval, pages 137--145, New Orleans, US. ACM Press, New York, US. Describes RCut, Scut, etc.  Home/Search Document Details and Download
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....are non category specific. 4.1.2 Thresholding The probabilistic outputs from the Bayesian classifiers can be used in various ways. The most direct way is to use the Bayes decision rule, p(y = 1 x, 0.5, to determine the relevance of the document described by x. However, as discussed in [10, 26], this is not optimal for the chosen evaluation measure. Therefore, in addition to 0.5 thresholding, we also empirically optimise the threshold for each category for the F1 measure on the training documents. This scheme, which we shall call MaxF1, has also been employed in [27] for thresholding ....
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....we obtain a ranked list of categories 9 for each test document; by further thresholding on the ranks or the scores, we obtain binary decisions, i.e. the categories above the threshold will be assigned to the document. There are advantages and disadvantages of di erent thresholding strategies [26, 27]. In this paper, we use the simplest strategy assigning the topranking category only to each document as a baseline; for more exible trade o between recall and precision, we further threshold on the scores of the top ranking candidates, as described in Section 4.4. 2.3.3. FOIL Quinlan s Foil ....
Yiming Yang. A study on thresholding strategies for text categorization. In W. Bruce Croft, David J. Harper, Donald H. Kraft, and Justin Zobel, editors, Proceedings of SIGIR-01, 24th ACM International Conference on Research and Development in Information Retrieval, pages 137-145, New Orleans, US, 2001. ACM Press, New York, US.
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