A rough set approach to multiple classifier systems

During the past decade methods of multiple classifier systems have been developed as a practical and effective solution for a variety of challenging applications. A wide number of techniques and methodologies for combining classifiers have been proposed in the past years in literature. In our work we present a new approach to multiple classifier systems using rough sets to construct classifier ensembles. Rough set methods provide us with various useful techniques of data classification. In the paper, we also present a method of reduction of the data set with the use of multiple classifiers. Reduction of the data set is performed on attributes and allows to decrease the number of conditional attributes in the decision table. Our method helps to decrease the number of conditional attributes of the data with a small loss on classification accuracy.