• DocumentCode
    2251645
  • Title

    Modified Reduct: Nearest Neighbor Classification

  • Author

    Ishii, Naohiro ; Torii, Ippei ; Bao, Yongguang ; Tanaka, Hidekazu

  • Author_Institution
    Dept. of Inf. Sci., Aichi Inst. of Technol., Toyota, Japan
  • fYear
    2012
  • fDate
    May 30 2012-June 1 2012
  • Firstpage
    310
  • Lastpage
    315
  • Abstract
    Dimension reduction of data is an important theme as in the data processing and on the web to represent and manipulate higher dimensional data. Rough set developed is fundamental and useful to process higher dimensional data. Reduct in the rough set is a minimal subset of features, which has almost the same discernible power as the entire features in the higher dimensional scheme. Then, there are relations between reducts and their classification classes. Here, we develop a method which connects reducts and the nearest neighbor method to classify data with higher classification accuracy. To improve the classification ability of reducts, we propose a new modified reduct and its optimization method for the classification with higher accuracy. Then, it is shown that the modified reduct improves the classification accuracy, which is followed by the optimized nearest neighbor classification.
  • Keywords
    Internet; data reduction; data structures; pattern classification; rough set theory; Web; data classification; data dimension reduction; data processing; dimensional data manipulation; dimensional data representation; modified reduct; nearest neighbor classification; optimization method; rough sets theory; Accuracy; Classification algorithms; Euclidean distance; Information science; Information systems; Reliability; Training; classification; dimension reduction; nearest neighbor classification; reduct;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Information Science (ICIS), 2012 IEEE/ACIS 11th International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4673-1536-4
  • Type

    conf

  • DOI
    10.1109/ICIS.2012.72
  • Filename
    6211115