›› 2017, Vol. 2 ›› Issue (12): 16-20.

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The Classification of K-Nearest Neighbor over Uncertain Data Based on Expected Semantic Distance

  

  1. (College of Mathematics and Computer, Dali University, Dali, Yunnan 671003, China)
  • Received:2017-05-12 Online:2017-12-15 Published:2017-12-15

Abstract: The uncertainty of data mainly includes the uncertainty of tuples and that of attribute values. For the latter type, a knearest neighbor classifier is proposed. The attribute value in this classifier is discrete, and the uncertainty of it is expressed by probability distribution vector. The semantic distance among probability distributions is firstly computed according to Concept Hierarchy Tree, and then the semantic distances among attributes and objects are computed. The classification accuracy rate has been validated by experimentation, which indicates that this classifier is a highly effective algorithm for uncertain data.

Key words: classification, KNN classifier, uncertain data, expected semantic distance

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