[1] P. Dongare, S. Kannan, R. Garg, and S. S. Harsoor, “Describing and displaying numerical and categorical data,” Airway, vol. 2, no. 2, p. 64, 2019.
[2] A. Agresti, An introduction to categorical data analysis. Hoboken, New Jersey: John Wiley & Sons, Inc., 2018.
[3] D. Jurafsky and J. H. Martin, “Speech and language processing: An introduction to natural language processing, computational linguistics and speech recognition” (draft), 2023.
[4] G. Alfonso Perez and R. Castillo, “Categorical variable mapping considerations in classification problems: Protein application,” Mathematics, vol. 11, no. 2, p. 279, 2023.
[5] J. T. Hancock and T. M. Khoshgoftaar, “Survey on categorical data for neural networks,” J. Big Data, vol. 7, no. 1, 2020.
[6] A. R. Shabrandi, A. Rajabzadeh Ghatari, N. Tavakoli, M. Dehghan Nayeri, & S. Mirzaei. “Fast COVID-19 Infection Prediction with In-House Data Using Machine Learning Classification Algorithms: A Case Study of Iran,” Journal of AI and Data Mining, vol. 11, no. 4, pp. 573-585, 2023.
[7] J. Moeyersoms and D. Martens, “Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector,” Decis. Support Syst., vol. 72, pp. 72–81, 2015.
[8] D. Reilly, M. Taylor, P. Fergus, C. Chalmers, and S. Thompson, “The categorical data conundrum: Heuristics for classification problems—A case study on domestic fire injuries,” IEEE Access, vol. 10, pp. 70113–70125, 2022.
[9] A. Alexandridis, E. Chondrodima, N. Giannopoulos, and H. Sarimveis, “A fast and efficient method for training categorical radial basis function networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 11, pp. 2831–2836, 2016.
[10] L. A. S. Cardona, H. D. Vargas-Cardona, P. Navarro González, D. A. Cardenas Peña, and Á. Á. Orozco Gutiérrez, “Classification of categorical data based on the Chi-square dissimilarity and t-SNE,” Computation (Basel), vol. 8, no. 4, p. 104, 2020.
[11] M. M. Arat, “Learning from high-cardinality categorical features in deep neural networks,” Journal of Advanced Research in Natural and Applied Sciences, 2022.
[12] D. B. Suits, “Use of dummy variables in regression equations,” J. Am. Stat. Assoc., vol. 52, no. 280, pp. 548–551, 1957.
[13] K. Potdar, T. S., and C. D., “A comparative study of categorical variable encoding techniques for neural network classifiers,” Int. J. Comput. Appl., vol. 175, no. 4, pp. 7–9, 2017.
[14] D. Micci-Barreca, “A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems,” SIGKDD Explor., vol. 3, no. 1, pp. 27–32, 2001.
[15] C. Seger, “An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing,” DEGREE PROJECT TECHNOLOGY. Published online 2018.
[16] W. H. Riska, D. Permana, A. A. Putra, and Zilrahmi, “Categorical data clustering with K-Modes method on fire cases in DKI Jakarta Province,” ujsds, vol. 2, no. 1, pp. 56–63, 2024.
[17] H. Cho and Y. Chung, “Clustering high-cardinality categorical data using category embedding methods,” J. Korean Data Inf. Sci. Soc., vol. 31, no. 1, pp. 209–220, 2020.
[18] T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” arXiv [cs.CL], 2013.
[19] J. Pennington, R. Socher, and C. Manning, “Glove: Global vectors for word representation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014.
[20] A. Asuncion and D. Newman, UCI machine learning repository, 2007.
[21] B. W. Matthews, “Comparison of the predicted and observed secondary structure of T4 phage lysozyme,” Biochim. Biophys. Acta, vol. 405, no. 2, pp. 442–451, 1975.
[22] M. T. Hagan and M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm,” IEEE Trans. Neural Netw., vol. 5, no. 6, pp. 989–993, 1994.
[23] R. Fernandes de Mello and M. Antonelli Ponti, “Statistical learning theory,” in Machine Learning, Cham: Springer International Publishing, 2018, pp. 75–128.
[24] G. H. John and P. Langley, “Estimating continuous distributions in Bayesian classifiers,” arXiv [cs.LG], 2013.
[25] H. Zhang, B. Quost, and M.-H. Masson, “Cautious weighted random forests,” Expert Syst. Appl., vol. 213, no. 118883, p. 118883, 2023.
[26] K.-L. Du and M. N. S. Swamy, “Radial Basis Function Networks,” in Neural Networks and Statistical Learning, London: Springer London, pp. 299–335, 2014.
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