Classification Based on Predictive Association Rules of Incomplete Data

この論文にアクセスする

この論文をさがす

著者

    • YOON Jeonghun
    • School of Computer Science and Engineering, Chung-Ang University
    • KIM Dae-Won
    • School of Computer Science and Engineering, Chung-Ang University

抄録

Classification based on predictive association rules (CPAR) is a widely used associative classification method. Despite its efficiency, the analysis results obtained by CPAR will be influenced by missing values in the data sets, and thus it is not always possible to correctly analyze the classification results. In this letter, we improve CPAR to deal with the problem of missing data. The effectiveness of the proposed method is demonstrated using various classification examples.

収録刊行物

  • IEICE transactions on information and systems

    IEICE transactions on information and systems 95(5), 1531-1535, 2012-05-01

    一般社団法人 電子情報通信学会

参考文献:  5件中 1-5件 を表示

各種コード

  • NII論文ID(NAID)
    10030943341
  • NII書誌ID(NCID)
    AA10826272
  • 本文言語コード
    ENG
  • 資料種別
    SHO
  • ISSN
    09168532
  • データ提供元
    CJP書誌  J-STAGE 
ページトップへ