Abstract
As a simple, effective and nonparametric classification method, kNN algorithm is widely used in text classification. However, there is an obvious problem: when the density of training data is uneven it may decrease the precision of classification if we only consider the sequence of first k nearest neighbors but do not consider the differences of distances. To solve this problem, we adopt the theory of fuzzy sets, constructing a new membership function based on document similarities. A comparison between the proposed method and other existing kNN methods is made by experiments. The experimental results show that the algorithm based on the theory of fuzzy sets (fkNN) can promote the precision and recall of text categorization to a certain degree.
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Shang, W., Huang, H., Zhu, H., Lin, Y., Wang, Z., Qu, Y. (2005). An Improved kNN Algorithm – Fuzzy kNN. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_109
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DOI: https://doi.org/10.1007/11596448_109
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
Online ISBN: 978-3-540-31599-5
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