Abstract—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.
Index Terms—Rough set, discernibility matrix, reduct, classification, FPGA .
K. S. Tiwari is with the MESCOE, Pune, Maharashtra, India.
A. G. Kothari and A. G. Keskar are with the VNIT, Nagpur, Maharashtra, India.
Cite: Kanchan S. Tiwari, Ashwin G. Kothari, and Avinash G. Keskar, "Reduct Generation from Binary Discernibility Matrix: An Hardware Approach," International Journal of Future Computer and Communication vol. 1, no. 3, pp. 270-272, 2012.
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