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Rough set theory is a powerful mathematical tool used for extracting useful rules from a huge database. We have proposed a Rough Set Machine which generates rules for classification applications. The classification task concentrates on predicting the value of the decision class for an object among a predefined set of classes' values. This rough set machine uses the concept of discernibility matrix for calculating the reducts, and using these reducts it generates the rules which are used for classifying the objects. The Reduct block is synthesized and downloaded on FPGA.

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Rough set theory (RST) is a relatively new mathematical theory used in, discovery of data dependencies, evaluation of significance of attributes and objects, reduction of data and meaningful rules generation from large databases. In this paper, a rough set approach is used for generation of reduct and classification rules. Attribute reduction is an important process of knowledge discovery. This paper proposes a hybridized attribute reduction algorithm which deals with inconsistent data, based on the concept of attribute frequency in the binary discernibility matrix. The information system is checked for inconsistencies and then simplified using Inconsistency Removal algorithm for finding equivalence classes. The simplified decision table is used for computing approximate reduct and based on it; rules are extracted from the database. The results are explained with the help of an example. MATLAB based simulation results are shown for various databases of UCI Machine Repository. In addition, rough set reduct generation accuracy is verified by RSES software. The study showed that the rough set theory is a useful tool for inductive learning and a valuable aid for building expert system mimicking human being.