DocumentCode
1875358
Title
A Novel Heuristic Text Classification Algorithm Based on Support Vector Machines
Author
Chen, Henian ; Yan, Lili
Author_Institution
Dept. of Software Eng., Hainan Software Profession Inst., Qionghai, China
fYear
2010
fDate
10-12 Dec. 2010
Firstpage
1
Lastpage
3
Abstract
Support Vector Machines (SVM), one of the new techniques for text classification, have been widely used in many application areas. SVM try to find an optimal hyperplane within the input space so as to correctly classify the binary classification problem. We present a novel heuristic text classification approach based on genetic algorithm (GA) and SVM. Simulation results demonstrate that GA and SVM are integrated effectively, and have good classification accuracy.
Keywords
genetic algorithms; pattern classification; support vector machines; text analysis; binary classification problem; genetic algorithm; heuristic text classification algorithm; optimal hyperplane; support vector machine; Algorithm design and analysis; Classification algorithms; Decision trees; Gallium; Support vector machines; Text categorization; Training;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Software Engineering (CiSE), 2010 International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-5391-7
Electronic_ISBN
978-1-4244-5392-4
Type
conf
DOI
10.1109/CISE.2010.5676971
Filename
5676971
Link To Document