• 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