Transforming continuous attributes using GA for applications of Rough Set Theory to control centers

Abstract

One of the possible application of Rough Sets Theory (RST) is the knowledge extraction in databases. Also, RST is useful to develop models for decision-making. During both processes one of the steps is the transformation of attributes with continuous values in digital values. This transformation sometimes can lose information. This paper presents a method for this transformation using genetic algorithms (GA). GA is used to determine the cut-off points for each attribute, getting a consistent transformation. An application in Control Centers with real data is presented.