References

Ali, R., M.H. Siddiqi and S. Lee, 2015. Rough set-based approaches for discretization: A compact review. Artif. Intell. Rev., 44: 235-263.
CrossRef  |  Direct Link  |  

Azar, A.T., H.H. Inbarani and K.R. Devi, 2017. Improved dominance rough set-based classification system. Neural Comput. Appl., 28: 2231-2246.
CrossRef  |  Direct Link  |  

Bache, K. and M. Lichman, 2013. UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CA. http://archive.ics.uci.edu/ml/citation_policy.html

Chen, D., D.W. Cui, C.X. Wang and Z.R. Wang, 2006. A rough set-based hierarchical clustering algorithm for categorical data. Intl. J. Inf. Technol., 12: 149-159.
Direct Link  |  

Dai, J. and H. Tian, 2013. Entropy measures and granularity measures for set-valued information systems. Inf. Sci., 240: 72-82.
CrossRef  |  Direct Link  |  

Dai, J., D. Huang, H. Su, H. Tian and T. Yang, 2014. Uncertainty measurement for covering rough sets. Intl. J. Uncertainty, Fuzziness Knowledge Based Syst., 22: 217-233.
CrossRef  |  Direct Link  |  

Herawan, T., I.T.R. Yanto and M.M. Deris, 2010. A Construction of Hierarchical Rough Set Approximations in Information Systems using Dependency of Attributes. In: Advances in Intelligent Information and Database Systems: Studies in Computational Intelligence, Nguyen, N.T., R. Katarzyniak and S.M. Chen (Eds.). Springer, Berlin, Germany, ISBN:978-3-642-12089-3, pp: 3-15.

Ho, T.B. and N.B. Nguyen, 2002. Nonhierarchical document clustering based on a tolerance rough set model. Int. J. Intell. Syst., 17: 199-212.
CrossRef  |  

Hu, Y.C., 2015. Flow-based tolerance rough sets for pattern classification. Appl. Soft Comput., 27: 322-331.
CrossRef  |  Direct Link  |  

Huang, Y., T. Li, C. Luo, H. Fujita and S.J. Horng, 2017. Dynamic variable precision rough set approach for probabilistic set-valued information systems. Knowl. Based Syst., 122: 131-147.
CrossRef  |  Direct Link  |  

Ibrahim, N., T. Hamza and E. Radwan, 2011. An evolutionary machine learning algorithm for classifying thyroid diseases diagnoses. Egyptian Comput. Sci. J., 35: 73-86.

Jusoh, N.A., J.M. Zain, N.K.N. Ismail and T.A.A. Kadir, 2007. Comparison between techniques in feature extraction. Proceedings of the International Conference on Electrical Engineering and Informatics, June 17-19, 2007, Institut Teknologi Bandung, Bandung, Indonesia, ISBN:978-979-16338-0-2, pp: 973-975.

Mac Parthalain, N. and Q. Shen, 2009. Exploring the boundary region of tolerance rough sets for feature selection. Pattern Recognition, 42: 655-667.
CrossRef  |  

Radwan, E. and A.M. Assiri, 2013. Thyroid diagnosis based technique on rough sets with modified similarity relation. Intl. J. Adv. Comput. Sci. Appl., 4: 120-126.
CrossRef  |  Direct Link  |  

Ramanna, S., 2011. Near tolerance rough sets. Appl. Math. Sci., 5: 1895-1902.
Direct Link  |  

Rivero, L.C., J.H. Doorn and V.E. Ferraggine, 2005. Rough Sets: Encyclopedia of Database Technologies and Applications. Idea Group Reference, Hershey, London, ISBN:9781591407959, Pages: 784.

Sun, L., J. Xu and Y. Li, 2014. A feature selection approach of inconsistent decision systems in rough set. J. Comput., 9: 1333-1340.
CrossRef  |  Direct Link  |  

Sun, L., J. Xu and Y. Tian, 2012. Feature selection using rough entropy-based uncertainty measures in incomplete decision systems. Knowl. Based Syst., 36: 206-216.
CrossRef  |  

Wang, F., J. Liang and C. Dang, 2013. Attribute reduction for dynamic data sets. Appl. Soft Comput., 13: 676-689.
CrossRef  |  Direct Link  |  

Wu, C. and L. Wang, 2008. An improved limited and variable precision rough set model for rule acquisition based on pansystems methodology. Kybernetes, 37: 1264-1271.
Direct Link  |  

Wu, J., D.Z. Tian, L. Wang and S.J. Yan, 2009. Rough set model based on credibility measure. Proceedings of the 2009 International Conference on Machine Learning and Cybernetics Vol. 3, July 12-15, 2009, IEEE, Hebei, China, ISBN:978-1-4244-3702-3, pp: 1822-1827.

Wu, W.Z., Y. Qian, T.J. Li and S.M. Gu, 2017. On rule acquisition in incomplete multi-scale decision tables. Inf. Sci., 378: 282-302.
CrossRef  |  Direct Link  |