How Good Are Probabilistic Approximations for Rule Induction from Data with Missing Attribute Values?
The main objective of our research was to test whether the probabilistic approximations should be used in rule induction from incomplete data. Probabilistic approximations, well known for many years, are used in variable precision rough set models and similar approaches to uncertainty.
For our experiments we used five standard data sets. Three data sets were incomplete to begin with and two data sets had missing attribute values that were randomly inserted. We used two interpretations of missing attribute values: lost values and “do not care” conditions. Among these ten combinations of a data set and a type of missing attribute values, in one combination the error rate (the result of ten-fold cross validation) was smaller than for ordinary approximations; for other two combinations, the error rate was larger than for ordinary approximations.
KeywordsImage Segmentation Probabilistic Approximation Decision Table Rule Induction Indiscernibility Relation
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