A Semi-supervised Text Classification Method Based on Incremental EM Algorithm

Author

Fan, Xinghua ; Guo, Zhiyi

Author_Institution

Coll. of Comput. Sci. & Technol., Chongqing Univ. of Posts & Telecommun., Chongqing, China

Volume

2

fYear

2010

fDate

14-15 Aug. 2010

Firstpage

211

Lastpage

214

Abstract

In the standard EM-based semi-supervised text classification, the classification performance is not well when the initial labeled samples are a few. How to improve the performance is an important issue. In view of this, a semi-supervised method based on incremental EM algorithm is proposed. This method makes full use of the useful information of intermediate classifier. On the one hand, this method verifies the feasibility of division existed in unlabeled samples, and uses the division mechanism to enhance the reliability of new incremental samples by dividing the unlabeled samples scientifically; on the other hand, a feedback learning mechanism is proposed, and it is used to decrease the probability of adding misclassified samples. Experimental results show that the classification performance is improved in our method.

Keywords

iterative methods; learning (artificial intelligence); pattern classification; text analysis; feedback learning mechanism; incremental EM algorithm; intermediate classifier; iterative algorithms; semisupervised text classification method; Classification algorithms; Learning systems; Medical services; Niobium; Reliability; Text categorization; Training; EM algorithm; division mechanism; feedback learning; semi-supervised learning; text classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Engineering (ICIE), 2010 WASE International Conference on

Conference_Location

Beidaihe, Hebei

Print_ISBN

978-1-4244-7506-3

Electronic_ISBN

978-1-4244-7507-0

Type

conf

DOI

10.1109/ICIE.2010.146

Filename

5571349