Title :
KNN text categorization algorithm based on LSA reduce dimensionality
Author :
Liangjun Li ; Yuanyuan Che ; Hongliang Zhang ; Tienan Li ; Ming Yang
Author_Institution :
Comput. Center, Anshan Normal Univ., Anshan, China
Abstract :
Aimed at the problem of document automatic classification, an improved KNN algorithm is proposed based on LSA reduced dimensionality. It advances the KNN algorithm´s efficiency and classifier´s precision by using LSA to reduce dimensionality of text feature matrix. The experiment result shows that the improved KNN algorithm has good performance.
Keywords :
pattern classification; text analysis; KNN text categorization; LSA reduced dimensionality; document automatic classification; text feature matrix; Algorithm design and analysis; Classification algorithms; Matrix decomposition; Semantics; Support vector machine classification; Text categorization; Training; KNN; latent semantic analysis; reduced dimensionality; text categorization;
Conference_Titel :
Information Technology and Artificial Intelligence Conference (ITAIC), 2011 6th IEEE Joint International
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-8622-9
DOI :
10.1109/ITAIC.2011.6030280
