1. Bühlmann, H., Gisler, A.: A Course in Credibility Theory and its Applications. Springer, Heidelberg (2006).
https://doi.org/10.1007/3-540-29273-X
2. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. JMLR 7, 1–30 (2006).
http://www.jmlr.org/papers/volume7/demsar06a/demsar06a.pdf
3. Fayyad, U.M., Irani, K.B.: Multi-Interval discretization of Continuous-Valued attributes for classification learning. In: Proceedings of the Conference of International Joint Conferences on Artificial Intelligence (1993).
https://www.semanticscholar.org/paper/1dc53b91327cab503acc0ca5afb9155882b717a5
4. Fernández-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. (2014).
http://www.jmlr.org/papers/volume15/delgado14a/delgado14a.pdf
5. Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32(200), 675–701 (1937).
https://www.tandfonline.com/doi/abs/10.1080/01621459.1937.10503522