Title :
Text classification based on SMO and fuzzy model
Author :
Mengqi Pei ; Xing Wu
Author_Institution :
Sch. of Comput. Eng. & Sci., Shanghai Univ., Shanghai, China
Abstract :
In this article we propose a text classification system using chi-value as feature selection method and SMO (sequential minimal optimization) algorithm as classifier. In addition, we use fuzzy model of fuzzy concept to describe documents´ classified label and entropy to calculate the uncertainty of a document´s classification result. Experimental results demonstrated that the proposed method can reach 87% or higher accuracy of text classification.
Keywords :
fuzzy set theory; optimisation; statistical analysis; text analysis; SMO; chi-value; document classification; entropy; feature selection method; fuzzy model; sequential minimal optimization; text classification; Classification algorithms; Entropy; Feature extraction; Support vector machine classification; Text categorization; Training; SMO; entropy; fuzzy concept; fuzzy model; text classification;
Conference_Titel :
Information Technology and Artificial Intelligence Conference (ITAIC), 2014 IEEE 7th Joint International
Print_ISBN :
978-1-4799-4420-0
DOI :
10.1109/ITAIC.2014.7065056
