Title :
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
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;
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
DOI :
10.1109/FITME.2009.93
