Bibliographical references

CLARK, P. Y BOSWELL, R. (1991). “Rule Induction with CN2: Some Recent Improvements”, en Kodratoff, Y. (Ed.): Machine Learning - Proceedings of the Fifth European Conference (EWSL-91), Springer-Verlag, Berlin, pp. 151-163.

FREUND, Y. y SCHAPIRE, R.E. (1997). “A decision-theoretic generalization of online learning and an application to boosting”. Journal of Computer and System Sciences, vol. 55(1), pp. 119-139.

NIBLETT, T. (1987). “Constructing decision trees in noisy domains”, en Bratko, I. y Lavrač, N. (Eds.): Progress in Machine Learning (proceedings of the 2nd European Working Session on Learning), Sigma, Wilmslow, UK, pp. 67-78.

PAWLAK, Z. (1991). Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht, Boston, London.

PREDKI, B., SLOWINSKI, R., STEFANOWSKI, J., SUSMAGA, R. y WILK, S. (1998). “ROSE – Software Implementation of the Rough Set Theory”, en POLKOWSKI, L. y SKOWRON, A. (Eds.): Rough Sets and Current Trends in Computing, Lecture Notes in Artificial Intelligence, vol. 1424. Springer-Verlag, Berlin, pp. 605-608.

PREDKI, B. y WILK, S. (1999). “Rough Set Based Data Exploration Using ROSE System”, en RAS, Z.W. y SKOWRON, A. (Eds.): Foundations of Intelligent Systems, Lecture Notes in Artificial Intelligence, vol. 1609, Springer-Verlag, Berlin, pp. 172-180.

QUINLAN, J.R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann Publishers, Inc., California.

QUINLAN, J.R. (1997). See5 (en Internet, http:www.rulequest.com/see5-info.html).

SEGOVIA, M.J. (2003). Predicción de crisis empresariales en seguros no vida mediante la metodología Rough Set, Tesis Doctoral, Universidad Complutense de Madrid.

SEGOVIA, M.J., GIL, J.A., HERAS, A. y VILAR, J.L. (2003) “La metodología Rough Set frente al Análisis Discriminante en los problemas de clasificación multiatributo”, comunicación presentada a las XI Jornadas ASEPUMA, Oviedo.