A probabilistic rough set approach to rule discovery

Abstract

Rough set theory is a relative new tool that deals with vagueness and uncertainty inherent in decision making. In this paper we suggest a new rough algorithm for reducing dimensions and extracting rules of information systems using expert systems. This paper introduces a probabilistic rough set approach to discover grade rules of transformer evaluation when there is a missing failure symptom of transformer. The core of the approach is a soft hybrid induction system called the Generalized Distribution Table and Rough Set System (GDT-RS) for discovering classification rules. The system is based on a combination of Generalized Distribution Table (GDT) and the Rough Set methodologies. With every decision rule two conditional probabilities associated, namely the certainty factor and the coverage factor. The probabilistic properties of the Decision rules are discussed. © 2011 Springer-Verlag.