Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data, Dordrecht (1991)
Greco, S., Matarazzo, B., Sowiski, R., Stefanowski, J.: An Algorithm for Induction of Decision Rules Consistent with the Dominance Principle. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 304-313. Springer, Heidelberg (2001)
Har-Peled, S., Roth, D., Zimak, D.: Constraint classification for multiclass classificatin and ranking. In: Advances in Neural Information Processing Systems, pp. 785-792 (2002)
Hullermeier, E., Furnkranz, J., Cheng, W., Brinker, K.: Label Ranking by learning pairwise preference. Artif. Intell. 172(16-17), 1897-1916 (2008)
Cheng, W., Huhn, J., Hullermeier, E.: Decision Tree and Instance-Based Learning for Labele Ranking. In: Proc. ICML 2009, International Conference on Machine Learning, Montreal, Canada (2009)
Aiolli, F., Sperduti, A.: A Preference Optimization Based Unifying Framework for Supervised Learning Problems. In: Furnkranz, J., Hullermeier, E. (eds.) Preference Learning, Springer, Heidelberg (2010)
Gartner, T., Vembu, S.: Label Ranking Algorithms: A Survey. In: Furnkranz, J., Hullermeier, E. (eds.) Preference Learning. Springer, Heidelberg (2010)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Recognition. John Wiley & Sons (2000)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Published by Morgan Kaufmann (2011)
Tan, P., Steinbach, M., Kumar, V.: Introduction to Data Mining. Published by Addison Wesley Longman (2006)
Dekel, O., Manning, C.D., Singer, Y.: Log-linear models for label ranking. In: Advances in Neural Information Processing Systems 16 (2003)
Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Advances in Neural Information Processing Systems 14 (2001)
Baszczyski, J., Sowiski, R., Szelg, M.: Sequential Covering Rule Induction Algorithm for Variable Consistency Rough Set Approaches. Information Sciences 181, 987-1002 (2011)
Baszczyski, J., Greco, S., Sowiski, R., Szelg, M.: Monotonic variable consistency rough set approaches. International Journal of Approximate Reasoning 50, 979-999 (2009)
Baszczyski, J., Greco, S., Sowiski, R.: Multi-criteria classification-a new scheme for application of dominance-based decision rules. European Journal of Operational Research 181, 1030-1044 (2007)
Doumpos, M., Zopounidis, C.: Multicriteria Decision Aid Classification Methods. Applied Optimization 73, 15-38 (2004)
Grzymaa-Busse, J.W.: Mining Numerical Data-A Rough Set Approach. In: Kryszkiewicz, M., Peters, J.F., Rybiski, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 12-21. Springer, Heidelberg (2007)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006)
Vincke, P.: L'aide Multicritere a la decision. Editions de l'ULB, Ellipses (1988)
Jacquet-Lagreze, E., Siskos, Y.: Preference disaggregation: 20 years of MCDA experience. EJOR 130, 233-245 (2001)
de Sa, C.R., Soares, C., Jorge, A.M., Azevedo, P., Costa, J.: Mining Association Rules for Label Ranking. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 432-443. Springer, Heidelberg (2011)
Bouyssou, D.: Ranking methods based on valued preference relations: A characterization of the net flow method. European Journal of Operational Research 60, 61-68 (1992)
Greco, S., Matarazzo, B., Sowiski, R.: Rough sets theory for multicriteria decision analysis. European Journal of Operational Research 129, 1-47 (2001)