Abstract
We show that excluding outliers from the training data significantly improves kNN classifier, which in this case performs about 10% better than the best know method—Centroid-based classifier. Outliers are the elements whose similarity to the centroid of the corresponding category is below a threshold.
Work supported by the MIC (Ministry of Information and Communication), Korea, under the Chung-Ang University HNRC-ITRC (Home Network Research Center) support program supervised by the IITA (Institute of Information Technology Assessment).
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Shin, K., Abraham, A., Han, S.Y. (2006). Improving kNN Text Categorization by Removing Outliers from Training Set. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2006. Lecture Notes in Computer Science, vol 3878. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11671299_58
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DOI: https://doi.org/10.1007/11671299_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32205-4
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