Study on Decision Classification Fusion Model

Author

Zhang, Xiao-Dan ; Niu, Zhen-Dong

Author_Institution

Sch. of Comput. Sci. & Technol., Beijing Inst. of Technol. Univ., Beijing, China

Volume

3

fYear

2009

fDate

12-14 Aug. 2009

Firstpage

107

Lastpage

109

Abstract

In this paper, a general decision layer classification fusion model, based on information fusion for improving classification precision, is proposed, that is, different multi-classification algorithms as the feature layer doing respective classification, and the results of classification algorithms are input into decision level, the last classification result is output.This model is applied into improving precision of text classification. And the model is used to the computer center of some department. Through the experiment, the text classification fusion model can improve the classification precision effectively.

Keywords

Bayes methods; classification; pattern classification; sensor fusion; support vector machines; text analysis; Bayes method; KNN; SVM; computer center; decision layer classification fusion model; information fusion; k-nearest neighbour classifier; multiclassification algorithm; support vector machine; text classification; Classification algorithms; Computer science; Explosives; Feature extraction; Hybrid intelligent systems; Internet; Natural languages; Nearest neighbor searches; Text categorization; Training data; classification algorithm; decision layer classificationfusion model; information fusion; text classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on

Conference_Location

Shenyang

Print_ISBN

978-0-7695-3745-0

Type

conf

DOI

10.1109/HIS.2009.234

Filename

5254544