Journal of Guangxi Normal University(Natural Science Edition) ›› 2012, Vol. 30 ›› Issue (1): 45-49.

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An Improvement Semi-supervised Learning Based on KNN Classification

LU Guang-quan, XIE Yang-cai, LIU Xing, ZHANG Shi-chao   

  1. College of Computer Science and Information Technology,GuangxiNormal University,Guilin Guangxi 541004,China
  • Received:2011-09-16 Online:2012-01-20 Published:2018-12-03

Abstract: An improved semi-supervised self-training classification learning algorithm is proposed based on K nearest neighbor,and several UCI data sets are used for experiments to improve the KNN-based semi-supervised classification model (self-training model) algorithm.The labeled datawhich gives the correct knowledge fromthe training is provided toget the final classification results.And the results show that the method can increase the classification accuracy dramatically.

Key words: semi-supervised learning, KNN classification, self-training

CLC Number: 

  • TP181
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