Normalized Decision Functions and Measures for Inconsistent Decision Tables Analysis

We consider the family of normalized decision functions acting over conditional frequency distributions computed from data tables. We draw the connection between such functions and approaches to generating inexact decision rules for the new case classification. We also introduce the family of normalized decision measures corresponding to particular decision functions. They enable us to express efficiency of particular strategies of reasoning with respect to a given data. We show the properties of approximate decision rules and decision reducts based on normalized decision functions and measures. As a result, we obtain an intuitive and flexible tool for extracting approximate classification models from data.