Abstract
KNN algorithm which is one of the best methods of text classifying in the vector space model (VSM) is a simple, example based and none-parameter method. But in the KNN algorithm, the fixed K value ignores the influence of the category and the document number of training text. So, selecting the correct K value can achieve better classification results. This paper proposes a kind of dynamic obtain k-valued for KNN classification algorithm, experimental results show that the dynamic obtain k-valued KNN classification algorithm with high performance.
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Gong, A., Liu, Y. (2011). Improved KNN Classification Algorithm by Dynamic Obtaining K. In: Shen, G., Huang, X. (eds) Advanced Research on Electronic Commerce, Web Application, and Communication. ECWAC 2011. Communications in Computer and Information Science, vol 143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20367-1_51
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DOI: https://doi.org/10.1007/978-3-642-20367-1_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20366-4
Online ISBN: 978-3-642-20367-1
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