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
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;
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
DOI :
10.1109/CISE.2010.5676971
