Title :
A kind of hybrid classification algorithm based on rough set and support vector machine
Author :
Wang, Lai-sheng ; Xu, Yi-Tian ; Zhao, Li-San
Author_Institution :
Coll. of Sci., China Agric. Univ., Beijing, China
Abstract :
Support vector machine (SVM) is a new machine learning method and it has greater generalization performance. Rough set theory is a new effective tool in dealing with vagueness and uncertainty information. Integrating the advantages of two approaches, a kind of hybrid classification algorithm to efficiently extract classification rules is proposed in the paper. Moreover, a new kind of attribute reduction algorithm by constructing generalized decision information table is presented in the paper. Finally they are applied into handwritten Chinese recognition, and result shows the validity and feasibility of the algorithm suggested in the paper.
Keywords :
character recognition; knowledge engineering; learning (artificial intelligence); pattern classification; rough set theory; support vector machines; table lookup; attribute reduction algorithm; classification rules; generalized information table; handwritten Chinese character recognition; hybrid classification algorithm; rough set theory; support vector machine; Classification algorithms; Data mining; Equations; Handwriting recognition; Machine learning; Machine learning algorithms; Set theory; Support vector machine classification; Support vector machines; Uncertainty; Attribute Reduction; Generalized Information Table; Rough Set Theory; Support Vector Machine;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527214