Web Document Classification Using MFA and MPM

Author

Sun, Xia ; Wang, Ziqiang

Author_Institution

Sch. of Inf. Sci. & Eng., Henan Univ. of Technol., Zhengzhou, China

fYear

2009

fDate

13-14 Dec. 2009

Firstpage

349

Lastpage

352

Abstract

Document classification has received extensive attention in the past decade due to its wide range applications. To efficiently deal with this problem, a novel document classification algorithm is proposed by using marginal fisher analysis (MFA) and minimax probability machine(MPM). Experimental results on the WebKB data set show that the proposed algorithm achieves much better performance than other related document classification algorithms.

Keywords

Internet; document handling; learning (artificial intelligence); minimax techniques; pattern classification; MFA; MPM; Web document classification; WebKB data set; marginal fisher analysis; minimax probability machine; Algorithm design and analysis; Classification algorithms; Data mining; Information analysis; Information science; Large scale integration; Linear discriminant analysis; Minimax techniques; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Future Information Technology and Management Engineering, 2009. FITME '09. Second International Conference on

Conference_Location

Sanya

Print_ISBN

978-1-4244-5339-9

Type

conf

DOI

10.1109/FITME.2009.93

Filename

5380999

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3106419