DocumentCode :
2835482
Title :
An improved KNN text categorization on skew sort condition
Author :
Haifeng, Liu ; Shousheng, Liu ; Zhan, Su
Author_Institution :
Inst. of Sci., PLA Univ. of Sci. & Technol., Nanjing, China
Volume :
7
fYear :
2010
fDate :
22-24 Oct. 2010
Abstract :
KNN is one of most frequent used methods for text categorization. The feature high-dimension and skew of sort distribution will impact the performance of the classifier. An improved KNN based on skew sort condition is introduced in this paper for solving the problem that the big swatch sort with more texts is easy to be selected when conducting the K neighbor selection. Firstly, text feature selection is conducted by an improved information gain method for more efficient using the categorization distribution information in the sample training set. Then an improved KNN classifier based on the sort is used for categorization, which can solve the problem that big swatch sort is selected in training set. The experiment shows this method has improved the KNN classification performance.
Keywords :
pattern classification; text analysis; KNN classifier; KNN text categorization; big swatch sort; skew sort condition; Information entropy; Variable speed drives; KNN; feature reduction; feature selection; text categorization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4244-7235-2
Electronic_ISBN :
978-1-4244-7237-6
Type :
conf
DOI :
10.1109/ICCASM.2010.5620491
Filename :
5620491
Link To Document :
بازگشت