Abstract
The sudden expansion of the web and the use of the Internet has caused some research fields to regain (or even increase) its old popularity. Of them, text categorization aims at developing a classification system for assigning a number of predefined topic codes to the documents based on the knowledge accumulated in the training process. In this paper, we investigate a text categorization method based on steepest descent induction algorithm combined with multi-level preference relation over retrieval output that is especially suitable for inducing classifiers over non-exclusive data set. Our framework enables us to define a threshold value for relativeness such a way that it becomes specific for each category. Furthermore, a cache memory of a category, which is obtained when training the classifier, makes text categorization adaptive. We have found out that a cache memory based on 8-4-2 (positive-boundary-negative) examples yielded almost true classifiers over Reuters-2178 data set.
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Sever, H., Bolat, Z., Raghavan, V.V. (2004). Use of Preference Relation for Text Categorization. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_89
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DOI: https://doi.org/10.1007/978-3-540-25929-9_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
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