DocumentCode
2123068
Title
Improved KNN classification algorithms research in text categorization
Author
Wang, Lijun ; Zhao, Xiqing
Author_Institution
HeBei North Univ., Zhang Jiakou, China
fYear
2012
fDate
21-23 April 2012
Firstpage
1848
Lastpage
1852
Abstract
Text classification is the important part of information retrieved and text mining, in text classification process, the traditional KNN classification algorithm´s calculation volume is huge and KNN classification of precision will fall when between the category have more common, in this basics, the improved KNN method is proposed, first the most likely k0 candidate category are got through Rocchio classification method, and then the part of representative sample are extracted in the k0 category training document. This method solves the above two problems to a certain extent, and has good results in the classification, improving classification performance.
Keywords
data mining; information retrieval; pattern classification; text analysis; Rocchio classification method; improved KNN classification algorithms research; information retrieval; text categorization; text classification; text mining; Accuracy; Algorithm design and analysis; Classification algorithms; Internet; Support vector machine classification; Text categorization; Training; Classes Center area; K-nearest Neighbor; Precision; Searching rates; text classification;
fLanguage
English
Publisher
ieee
Conference_Titel
Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on
Conference_Location
Yichang
Print_ISBN
978-1-4577-1414-6
Type
conf
DOI
10.1109/CECNet.2012.6201850
Filename
6201850
Link To Document