[Alv05]
Álvarez, D. Feature selection for data analysis using Rough Sets Theory. Thesis of Computer Science Engineering. Thesis Director: Yailé Caballero, M.Sc. University of Camagüey, Cuba. 2005.
[Ahn00]
Ahn, B.S. et al.. The integrated methodology of rough set theory and artificial neural networks for business failure predictions. Expert Systems with Applications 18, 65–74. 2000.
CrossRef[Bel98]
Bell, D. and Guan, J. Computational methods for rough classification and discovery. Journal of ASIS 49,5, pp. 403–414. 1998.
[Cab05]
Caballero, Y. Using Rough Sets Theory to treatment of the data. Thesis of Master in Computer Science. Thesis Director: Rafael Bello, PhD. Universidad Central de Las Villas, Cuba. 2005.
[Car98]
Carlin, U.S. et al.. Rough set analysis of medical datasets and A case of patient with suspected acute appendicitis. In ECAI 98 Workshop on Intelligent data analysis in medicine and pharmacology.
[Cho96]
Choubey, S.K. et al. A comparison of feature selection algorithms in the context of rough classifiers. In Proceedings of Fifth IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1122–1128. 1996.
[Cho99]
Chouchoulas, A. and Shen, Q. A rough set-based approach to text classification. Lectures Notes in Artificial Intelligence no. 1711, pp. 118–127. 1999.
[Deo95]
Deogun, J.S. et al. Exploiting upper approximations in the rough set methodology. In Proceedings of First International Conference on Knowledge Discovery and Data Mining, Fayyad, U. Y Uthurusamy, (Eds.), Canada, pp. 69–74. 1995.
[Deo98]
Deogun, J.S. et al. Feature selection and effective classifiers. Journal of ASIS 49,5, pp. 423–434. 1998.
[Dim66]
Dimitriev, A. N.; Zhuravlev, J. I.; Krendeleiev, F. P.. About mathematical principles of objects and phenomenon classification. Diskretnyi Analiz No. 7, pp. 3–15, 1966.
[Gre0l]
Greco, S. Et al. Rough sets theory for multicriteria decision analysis. European Journal of Operational Research 129, pp. 1–47, 2001.
MATHMathSciNetCrossRef[Jen03]
Jensen R. and Qiang, S. “Finding rough sets reducts with Ant colony optimization”. http://www.inf.ed.ac.uk/publications/online/0201.pdf 2003.
[Koc98]
Koczkodaj, W.W. et al.. Myths about Rough Set Theory. Comm. of the ACM, vol. 41, no. 11, nov. 1998.
[Koh94]
Kohavi, R. and Frasca, B. Useful feature subsets and Rough set Reducts. Proceedings of the Third International Workshop on Rough Sets and Soft Computing. 1994.
[Kom99a]
Komorowski, J. Pawlak, Z. et al.. Rough Sets: A tutorial. In Pal, S.K. and Skowron, A. (Eds) Rough Fuzzy Hybridization: A new trend in decision-making. Springer, pp. 3–98. 1999.
[Kom99b]
Komorowski, J. et al.. A Rough set perspective on Data and Knowledge. In The Handbook of Data mining and Knowledge discovery, Klosgen, W. and Zytkow, J. (Eds). Oxford University Press, 1999.
[Mau96]
Maudal, O. Preprocessing data for neural network based classifiers: Rough sets vs Principal Component Analysis. Project report, Dept. of Artificial Intelligence, University of Edinburgh. 1996.
[Muh98]
Mühlenbein H. The equation for the response to selection and its use for prediction. Evolutionary Computation 5(3), pp. 303–346, 1998.
[Muh99]
Mühlenbein, H; Mahnig, T.; Ochoa, A. Schemata, distributions and graphical models on evolutionary optimization. Journal of Heuristics, 5(2), pp. 215–247. 1999.
MATHCrossRef[Ohr97]
Ohrn, A. and Komorowski, J.. Rosetta: A rough set toolkit for analysis of data. In Proc. Third Int. Join Conference on Information Science, Durham, NC, USA, march 1–5, vol. 3, pp. 403–407. 1997.
[Pal99]
Pal, S.K. and Skowron, A. (Eds). Rough Fuzzy Hybridization: a new trend in decision-making. Springer-Verlag, 1999.
[Pal02]
Pal, S.K. et al. Web mining in Soft Computing framework: Relevance, State of the art and Future Directions. IEEE Transactions on Neural Networks, 2002.
[Paw82]
Pawlak, Z. Rough sets. International Journal of Information & Computer Sciences 11, 341–356, 1982.
MATHMathSciNetCrossRef[Paw91]
Pawlak, Z. Rough SetsTheoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht, 1991. En: http://citeseer.ist.psu.edu/context/36378.html
[Paw94]
Pawlak, Z. and Skowron, A. “Rough sets rudiments”. Bulletin of International Rough Set Society. Volume 3, Number 3. http://w\vw.kuenstliche-intelligenz.de/archiv/2001_3/pawlak.pdf
[Paw95]
Pawlak, Z. “Rough Sets, Rough Relations and Rough functions”. R. Yager, M. Fedrizzi, J. Keprzyk (eds.): Advances in the Dempster — Shafer Theory of Evidence, Wiley, New Cork, pp 251–271. 1995 http://citeseer.ist.psu.edu/105864.html
[Piñ03]
Piñero, P; Arco, L; García, M. and Caballero, Y. Two New Metrics for Feature Selection in Pattern Recognition. Lectures Notes in computer Science (LNCS 2905), pp. 488–497. Springer, Verlag, Berlin Heidelberg. New York. ISSN 0302-9743. ISBN 3-540-20590-X.
[Pol02]
Polkowski, L.. Rough sets: Mathematical foundations. Physica-Verlag, p. 574. Berlin, Germany. 2002.
[Pre98]
Predki, B. et al.. ROSE-Software implementation of the Rough Set Theory. In Polkowski, L. and Skowron, A. (Eds) Rough Sets and Current Trends in Computing, Proceedings of the RSCTC98 Conference. Lectures Notes in Artificial Intelligence vol. 1424, Berlin pp. 605–608.
[Tay02]
Tay, F.E. and Shen, L.. Economic and financial prediction using rough set model. European Journal of Operational Research 141, pp. 641–659. 2002.
MATHCrossRef[Wil98]
Wilson, Randall. Martinez, Tony R. Reduction Techniques for Exemplar-Based Learning Algorithms. Machine Learning. Computer Science Department, Brigham Young University. USA 1998.
[Wro95]
Wroblewski, J. Finding minimal reducts using genetic algorithms. In Wang, P.P. (Ed). Proceedings of the International Workshop on Rough Sets Soft Computing at Second Annual Joint Conference on Information Sciences, North Carolina, USA, p. 679, pp. 186–189. 1995.
[Wro96]
Wroblewski, J. Theoretical foundations of order-based genetic algorithms. Fundamenta Informaticae, vol. 28(3,4), pp. 423–430. IOS Press. 1996.
MATHMathSciNet[Wro98]
Wroblewski, J. Genetic algorithms in decomposition and classification problems. In Polkowski, L. and Skowron, A. (Eds.). Rough sets in Knowledge Discovery 1: Applications, Case Studies and Software Systems. Physica-Verlag, pp. 472–492. 1998.
[Zho0l]
Zhong, N. et al.. Using Rough sets with heuristics for feature selection. Journal of Intelligent Information Systems, 16, 199–214. 2001.
MATHCrossRef