Bibliographical references


McKee, T. (2000). Developing a Bankruptcy Prediction Model via Rough Sets Theory, International Journal of Intelligent Systems in Accounting, Finance and Management, nº 9, pp. 159-173.

Nurmi, H., Kacprzyk, J. y Fedrizzi, M. (1996). Probabilistic, fuzzy and rough concepts in social choice, European journal of Operational Research nº 95, pp. 264-277.

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., Wilk, S. (1998). ROSE Software Implementation of the Rough Set Theory, in L. Polkowski, A. Skowron, 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. In: Z.W. Ras, A. Skowron eds. Foundations of Intelligent Systems, Lecture Notes in Artificial Intelligence, vol. 1609, Springer-Verlag, Berlin, pp. 172-180.

Sanchis, A. (2000). Una aplicación del Análisis Discriminante a la previsión de la Insolvencia en las empresas españolas de seguros no-vida, Tesis Doctoral, Universidad Complutense de Madrid.

Skowron, A. (1991). The Implementation of Algorithms based on Discernibility Matrix, Manuscript.

Slowinski, R., ed. (1992). Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, Dordrecht, The Netherlands: Kluwer Academic Publishers.

Zopounidis, C., y Dimitras A. (1998). Multicriteria Decision Aid Methods for the Prediction of Business Failure. Kluwer. Dordrecht.