Text classification based on SMO and fuzzy model
- Resource Type
- Conference
- Authors
- Pei, Mengqi; Wu, Xing
- Source
- 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference Information Technology and Artificial Intelligence Conference (ITAIC), 2014 IEEE 7th Joint International. :306-310 Dec, 2014
- Subject
- Computing and Processing
Classification algorithms
Support vector machine classification
Feature extraction
Text categorization
Entropy
Training
text classification
SMO
fuzzy model
fuzzy concept
entropy
- Language
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.