Cited by 314
Chung-Ang University
ORCID: 0000-0001-7124-1141Publishes on Text and Document Classification Technologies, Image Retrieval and Classification Techniques, Face and Expression Recognition. 232 papers and 3.8k citations.
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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.