Classification Based on Predictive Association Rules of Incomplete Data

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概要

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• 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.

• 2012-05-01

著者

• Kim Dae-won

  School Of Computer Science And Engineering Chung-ang University

• YOON Jeonghun

  School of Computer Science and Engineering, Chung-Ang University

関連論文

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• Classifying Categorical Data Based on Adoptive Hamming Distance
• Classification Based on Predictive Association Rules of Incomplete Data

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