References

Jian, L., S. Liu and Y. Lin, 2010. Hybrid Rough Sets and Applications in Uncertain Decision-Making. CRC Press, USA., Pages: 261.

Munakata, T., 2008. Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More. 2nd Edn., Springer Verlag, USA., ISBN-13: 978-1846288388.

Pawlak, Z., 1982. Rough sets. Int. J. Comput. Inform. Sci., 11: 341-356.
CrossRef  |  Direct Link  |  

Pawlak, Z., 2010. Rough set theory and its applications to data analysis. Cybernet. Syst., 29: 661-688.
Direct Link  |  

Rosa, J.L., A. Robin, M.B. Silva, C.A. Baldan and M.P. Peres, 2009. Electro deposition of copper on titanium wires: Taguchi experimental design approach. J. Mater. Process. Technol., 209: 1181-1188.
CrossRef  |  

Roy, K.R., 1990. A Primer on the Taguchi Method. Van Nostrand Reinholds, New York.

Tripathy, B.K. and D.P. Acharjya, 2012. Approximation of classification and measures of uncertainty in RS on two Universal sets. VIT University. Int. J. Adv. Sci. Technol., 40: 77-90.
Direct Link  |  

Yao, Y.Y., 1998. A comparative study of fuzzy sets and rough sets. Inf. Sci., 109: 227-242.
CrossRef  |  Direct Link  |  

Yuan, Z., Y. Wu, G. Wang and J. Li, 2006. Motion-Information-Based Video Retrieval System Using Rough Pre-Classification. In: Transactions on Rough Sets V. F.P. James and A. Skowron (Eds.). Springer, Berlin, Germany, ISBN: 978-3-540-39382-5, pp: 306-333.

Zhao, Y., Y. Yao and F. Luo, 2007. Data analysis based on discernibility and indiscernibility. Inf. Sci., 177: 4959-4976.
Direct Link  |