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Abstract Details
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Activity Number:
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585
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #302882 |
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Title:
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Data Mining Categorical Predictors with Missing Values
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Author(s):
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Hua Fang*+ and Honggang Wang
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Companies:
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University of Massachusetts and University of Massachusetts at Dartmouth
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Address:
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Medical School, , ,
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Keywords:
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data mining ;
categorical predictor ;
missing values ;
high-dimensional ;
multiple-imputation ;
simulation
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Abstract:
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Predictors with categories of no clear boundaries are prevalent in all kinds of studies, for example, low- or high- cancer/behavioral risk people, better or worse consulting procedure, contrarian or non-contrarian investors, efficient or inefficient market, etc.. A number of attributes can be used to better describe this type of categorical predictors, accordingly reduce their measurement errors and increase their predictive power in statistical hypothesis testing. To accommodate missing values in the attributes, we proposed multiple-imputation based data mining techniques to characterize categorical predictors. Theoretical illustration, simulation and cases studies will be included to demonstrate the utility of our proposed method.
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