[1] E.Alpaydin, Introduction to Machine Learning, Adaptive Computation and Machine Learning,
MIT Press, Third Edition, 2014.
[2] C.M. Bishop, Pattern Recognition and Machine Learning, Volume 4 of Information Science
and Statistics, Springer, 2006.
[3] L. Dalton, E. Dougherty, Optimal Bayesian Classification, Press Monograph Series, SPIE Press,
2020.
[4] R. Duda, P. Hart, D. Stork, Pattern Classification, Wiley, 2012.
[5] A. Fern´andez, S. Garc´ıa, M. Galar, R.C. Prati, B. Krawczyk, F. Herrera, Data Intrinsic Charac-
teristics, pages 253–277, Springer, 2018.
[6] K. Fukunaga, Introduction to Statistical Pattern Recognition, Chapter 10, Academic Press,
1990.
[7] S. Guan, M.H. Loew, A novel intrinsic measure of data separability, 52 (2022) 17734–17750.
[8] T.K. Ho, M. Basu, Complexity measures of supervised classification problems, IEEE Trans.
Pattern Anal. Mach. Intell. 24 (2002) 289–300.
[9] A. Izenman, Modern Multivariate Statistical Techniques: Regression, Classification, and Man-
ifold Learning, Springer Texts in Statistics. Springer, 2009.
[10] A.C. Lorena, L.P.F. Garcia, J. Lehmann, M.C.P. Souto, T.K. Ho, How complex is your clas-
sification problem? A survey on measuring classification complexity, ACM Comput. Surv. 52
(2019) 1–34.
[11] G.J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition, Wiley, 2004.
[12] K. Murphy, Probabilistic Machine Learning: An Introduction, Adaptive Computation and Ma-
chine Learning series, MIT Press, 2022.
[13] M. Noshad, L. Xu, A. Hero, Learning to benchmark: Determining best achievable misclassifi-
cation error from training data, 2019, arXiv:1909.07192.
[14] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P.
Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M.
Perrot, E. Duchesnay, Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12 (2011)
2825–2830.
[15] Y. Peleg, Hungabunga: Brute-Force all sklearn models with all possible hyperparameters, and
rank using cross-validation, GitHub, Retrieved from https://github.com/ypeleg/HungaBunga,
2023.
[16] S. Theodoridis, Machine Learning: A Bayesian and Optimization Perspective, Elsevier, 2020.
[17] L. Wasserman, All of Statistics: A Concise Course in Statistical Inference, Springer, 2004.
[18] L. Xue, X. Zhang, W. Jiang, K. Huo, Q. Shen, A classification performance evaluation measure
considering data separability In L. Iliadis, A. Papaleonidas, P. Angelov, and C. Jayne, editors,
Artificial Neural Networks and Machine Learning – ICANN 2023, pages 1–13, Springer Nature
Switzerland, 2023.
[19] S. Yu, X. Li, Y. Feng, X. Zhang, S. Chen. An instance-oriented performance measure for clas-
sification. Inf. Sci. 580 (2021) 598–619.