期刊
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
卷 E95D, 期 5, 页码 1531-1535出版社
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
DOI: 10.1587/transinf.E95.D.1531
关键词
associative classification; CPAR; missing values
资金
- Basic Science Research through the National Research Foundation of Korea (NRF)
- Ministry of Education, Science and Technology [2011-0004113]
- National Research Foundation of Korea [2010-0012885] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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.
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