Optimal Decision Making with Data-Acquired Decision Tables

Abstract

The paper deals with predictive decision models acquired from data called probabilistic decision tables. The methodology of probabilistic decision tables presented in this article is derived from the theory of rough sets. In this methodology, the probabilistic extension of the original rough set theory, called variable precision model of rough sets, is used. The theory of rough sets is applicable to identification and characterization of dependencies occurring in data. Each identified dependency is represented in the form of a decision table which subsequently is analyzed and optimized using rough sets-based methods. The original model of rough sets is restricted to the analysis of functional, or partial functional dependencies. The variable precision model of rough sets extends the capabilities of the rough set model to identify probabilistic dependencies, probabilistic reducts and cores allowing for construction of probabilistic predictive models. The paper reviews the variable precision model of rough sets with the main focus on setting the parameters of the model and on decision strategies to maximize the expected gain from the decisions.KeywordsDecision TableDecision AttributeGain FunctionProbabilistic DependencyExpected GainThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.