Statistical analysis with missing data
Statistical analysis with missing data
C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
On Classification with Incomplete Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Selective classifiers can effectively improve the accuracy and efficiency of classification by deleting irrelevant or redundant attributes of a data set. Though many selective classifiers have been proposed, most of them deal with complete data. Yet actual data sets are often incomplete and have many redundant or irrelevant attributes. So constructing selective classifiers for incomplete data is important. In this paper a hybrid selective classifier for incomplete data, denoted as CBSD, is presented. The proposed algorithm needs no assumption about data sets that are necessary for previous methods of processing incomplete data in classification. Experiments results on twelve benchmark incomplete data sets show that CBSD can effectively improve the accuracy and efficiency of classification while enormously reducing the number of attributes.