Title :
Research of Text Classification Model Based on Latent Semantic Analysis and Improved HS-SVM
Author :
Zhang, Yu-feng ; He, Chao
Author_Institution :
Center for Studies of Inf. Resources, Wuhan Univ., Wuhan, China
Abstract :
A text classification model based on Latent Semantic Analysis and Improved Hyper-sphere Support Vector Machine, is proposed in order to improve the accuracy and efficiency of text classification. Latent Semantic Analysis is used in this model for feature extraction, eliminating the text representation errors caused by synonyms and polysemes, and reducing the dimension of text vector. At the same time, a new decision-making approach based on concentration, is designed for improving the classification of texts in overlapping regions. Experimental results show that the model will give good classification results when the number of classes is small. And the improved algorithm is effective and feasible.
Keywords :
decision making; feature extraction; support vector machines; text analysis; vectors; decision making; feature extraction; improved HS-SVM; improved hypersphere support vector machine; latent semantic analysis; text classification model; text representation error elimination; text vector dimension; Feature extraction; Information analysis; Information filtering; Information resources; Machine learning; Machine learning algorithms; Quadratic programming; Support vector machine classification; Support vector machines; Text categorization;
Conference_Titel :
Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5872-1
Electronic_ISBN :
978-1-4244-5874-5
DOI :
10.1109/IWISA.2010.5473702
