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.

Volume

3

fYear

2006

fDate

Aug. 30 2006-Sept. 1 2006

Firstpage

186

Lastpage

189

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on

Conference_Location

Beijing

Print_ISBN

0-7695-2616-0

Type

conf

DOI

10.1109/ICICIC.2006.381

Filename

1692147