A comparison of rule sets induced by techniques based on rough set theory

Abstract

The main goal of this paper is to investigate the performance of rule sets obtained by different techniques based on the rough set theory. The performance of a rule set is defined as its ability to classify a set of objects into predefined classes. The set of rules contains the if then form rules. The set of objects is represented by flat table (table, organized data) where each object is represented by its attributes. To generate the rule sets we have used Rosetta software system. The performance of rule sets is measured by employment of the confusion matrix.