Searching for Features Defined by Hyperplanes
نویسندگان
چکیده
We consider decision tables with real value conditional attributes and we present a method for extraction of features deened by hyperplanes in a multi-dimensional aane space. These new features are often more relevant for object classiication than the features deened by hyperplanes parallel to axes. The method generalizes an approach presented in 18] in case of hyperplanes not necessarily parallel to the axes. We propose genetic strategies searching for hyperplanes discerning between objects from diierent decision classes.
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