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- W2049817875 abstract "Using a loan application data set, this paper demonstrates the use of several graphical tools to assess information quality: histograms to study individual variables, scatter plots to compare original and cleaned variables as well as to examine the effects that cleaning a particular predictor has on models of a decision, decision trees to identify important predictors of a decision, and ROC curves to evaluate the predictive value of each attribute. Proposed techniques for cleaning a data set include eliminating erroneous records, excluding attributes with too many incorrect values from the model and applying domain knowledge. We suggest that our approach can be applied to a small sample of a data set to help prioritise which variables should be cleaned." @default.
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- W2049817875 date "2005-01-01" @default.
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- W2049817875 title "Graphical tools for assessing information quality: loan application decisions" @default.
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- W2049817875 doi "https://doi.org/10.1504/ijtpm.2005.008634" @default.
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