[20] Justin M. Johnson and Taghi M. Khoshgoftaar. "Encoding Techniques for High-Cardinality Features and Ensemble Learners". In: IRI. IEEE, 2021, pp. 355–361.
[21] John G Kemeny. “Mathematics without numbers”. In: Daedalus 88.4 (1959), pp. 577–591.
[22] Daniele Micci-Barrega. "A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems". In: SIGKDD Explor. 3.1 (2001), pp. 27-32.
[23] Erick Moreno-Centeno and Adolfo R Escobedo. "Axiomatic aggregation of incomplete rankings". In: IIE Transactions 48.6 (2016), pp. 475-488.
[24] S Moro, P Rita, and P Cortez. Bank Marketing. UCI Machine Learning Repository. 2012.
[25] *Mushroom. UCI Machine Learning Repository.* 1987.
[26] Engineering National Academies of Sciences, Medicine, et al. Reproducibility and replicability in science. National Academies Press, 2019.
[27] Christina Nießl et al. "Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results". In: WIREs Data Mining Knowl. Discov. 12.2 (2022).
[28] Florian Pargent et al. "Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features". In: Comput. Stat. 37.5 (2022), pp. 2671-2692.
[29] F. Pedregosa et al. "Scikit-learn: Machine Learning in Python". In: Journal of Machine Learning Research 12 (2011), pp. 2825-2830.
[30] Kedar Potdar, Taher S Pardawala, and Chinmay D Pai. “A comparative study of categorical variable encoding techniques for neural network classifiers”. In: International journal of computer applications 175.4 (2017), pp. 7-9.
[31] Liudmila Ostroumova Prokhorenkova et al. "CatBoost: unbiased boosting with categorical features". In: NeurIPS. 2018, pp. 6639-6649.
[32] Quinlan and Quinlan. Credit Approval. UCI Machine Learning Repository.
[33] Ross Quinlan. Statlog (Australian Credit Approval). UCI Machine Learning Repository.
[34] Ross Quinlan. Thyroid Disease. UCI Machine Learning Repository. 1987.
[35] Jan N van Rijn and Jonathan K Vis. "Endgame Analysis of Dou Shou Qi". In: ICGA Journal 37.2 (2014), pp. 120-124.
[36] Diogo Seca and João Mendes-Moreira. "Benchmark of Encoders of Nominal Features for Regression". In: WorldCIST (1). Vol. 1365. Advances in Intelligent Systems and Computing. Springer, 2021, pp. 146-155.
[37] Alen Shapiro. *Chess (King-Rook vs. King-Pawn)*. UCI Machine Learning Repository. 1989.
[38] Peter Sprent and Nigel C Smeeton. Applied nonparametric statistical methods. CRC press, 2016.
[39] Gero Szepannek. "On the practical relevance of modern machine learning algorithms for credit scoring applications". In: WIAS Report Series 29 (2017), pp. 88-96.
[40] Muhammad Usman and Adeel Ahmed. *Dresses_Attribute_Sales*. UCI Machine Learning Repository, 2014.
[41] Eric Valdez-Valenzuela, Angel Kuri-Morales, and Helena Gómez-Adorno. "Measuring the Effect of Categorical Encoders in Machine Learning Tasks Using Synthetic Data". In: MICAI (1). Vol. 13067. Lecture Notes in Computer Science. Springer, 2021, pp. 92–107.
[42] Joaquin Vanschoren et al. "OpenML: networked science in machine learning". In: SIGKDD Explor. 15.2 (2013), pp. 49-60.
[43] J. Wnek. MONK's Problems. UCI Machine Learning Repository. 1992.
[44] Marvin N Wright and Inke R König. "Splitting on categorical predictors in random forests". In: PeerJ 7 (2019), e6339.
[45] Yeawon Yoo and Adolfo R. Escobedo. “A New Binary Programming Formulation and Social Choice Property for Kemeny Rank Aggregation”. In: Decis. Anal. 18.4 (2021), pp. 296–320.
[46] H Peyton Young. "Condorcet's theory of voting". In: American Political science review 82.4 (1988), pp. 1231-1244.
[47] H Peyton Young and Arthur Levenglick. “A consistent extension of Condorcet's election principle”. In: SIAM Journal on applied Mathematics 35.2 (1978), pp. 285–300.