A Fuzzy Rough Sets Classifier for Database Mining

Object classifiers are important tool for relational database mining. These classifiers are trained by existing relations in the database and then are used for classifying new objects. Rough Set (RS) classifiers have been shown to be useful in classification queries. These classifiers do not, however, perform adequately when the attribute set of a new object does not precisely match the antecedent of classification rules. We combine the capabilities of the Fuzzy Set and Rough Set theories to create a Fuzzy-Rough Set (FRS) classifier that alleviates this shortcoming of the RS classifiers. For a data set we perform a statistical analysis of the classification power of the proposed classifier. In addition, we compare the performance of the new classifier with that of three other classifiers, using eight additional data sets. All data sets were obtained from actual experiments. The results show that the FRS classifier outperforms the other three classifiers.