Control of Variables in Reducts - kNN Classification with Confidence

  • Naohiro Ishii
  • Yuichi Morioka
  • Yongguang Bao
  • Hidekazu Tanaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6884)

Abstract

Reduct in rough set is a minimal subset of features, which has almost the same discernible power as the entire features. Then, there are relations between reducts and the classification classes. Here, we propose multiple reducts which are followed by the k-nearest neighbor with confidence to classify documents with higher classification accuracy. To improve the classification accuracy, some reducts are needed for the classification. Then, control of variables as attributes are important for the classification. To select better reducts for the classification, a greedy algorithm is developed here for the classification, which is based on the selection of useful attributes These proposed methods are verified to be effective in the classification on benchmark datasets from the Reuters 21578 data set.

Keywords

Classification Accuracy Greedy Algorithm Confidence Computation High Classification Accuracy Train Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Naohiro Ishii
    • 1
  • Yuichi Morioka
    • 1
  • Yongguang Bao
    • 2
  • Hidekazu Tanaka
    • 3
  1. 1.Aichi Institute of TechnologyToyotaJapan
  2. 2.Aichi Information SystemKariyaJapan
  3. 3.Daido UniversityNagoyaJapan

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