DocumentCode
593143
Title
Mathematical Analysis on Weight Vectors in Text Classification
Author
Fengxi Song ; Qinglong Chen ; Zhongwei Guo ; Xiumei Gao
Author_Institution
Dept. of Autom. & Simulation, New Star Res. Inst. of Appl. Tech. in Hefei City, Hefei, China
fYear
2012
fDate
6-8 Nov. 2012
Firstpage
148
Lastpage
151
Abstract
By means of rigid mathematical deductions we prove that weight vectors cannot promote the performance of the optimal classifier, i.e. the Bayesian classifier in terms of the error, F-one score, or breakeven point. The conclusion is important in that people used to promote the performance of a classifier by trying various weight vectors in text classification.
Keywords
Bayes methods; classification; mathematical analysis; text analysis; Bayesian classifier; F-one score; breakeven point; mathematical analysis; mathematical deductions; text classification; weight vectors; Bayesian methods; Classification algorithms; Mathematical model; Support vector machine classification; Text categorization; Training; Vectors; evaluation; optimal classifier; text classification; weight vector;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems (GCIS), 2012 Third Global Congress on
Conference_Location
Wuhan
Print_ISBN
978-1-4673-3072-5
Type
conf
DOI
10.1109/GCIS.2012.14
Filename
6449505
Link To Document