Title :
A Fast Document Classification Algorithm Based on Improved KNN
Author :
Guo, Ge ; Ping, Xijian ; Chen, Gang
Author_Institution :
Dept. of Inf. Sci., Zhengzhou Inf. Sci. & Technol. Inst.
fDate :
Aug. 30 2006-Sept. 1 2006
Abstract :
A novel KNN classification algorithm combining model and evidence theory is proposed in this paper. The new method not only overcomes the main shortage of lazy learning in traditional KNN, but also takes the distances between samples to be recognized and samples in k-neighbors into account. At the same time the method resolves the unrecognizable cases of unknown samples. Applying the classification algorithm into the document recognition, experimental results show its satisfied recognition rate and fast categorization speed
Keywords :
classification; document handling; learning (artificial intelligence); pattern classification; uncertainty handling; document categorization; document classification algorithm; document recognition; evidence theory; k-nearest neighbor classification algorithm; lazy learning method; model theory; Bayesian methods; Classification algorithms; Diversity reception; Hidden Markov models; Information science; Internet; Software libraries; Spatial databases; Uncertainty;
Conference_Titel :
Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7695-2616-0
DOI :
10.1109/ICICIC.2006.381
