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oai:uvadoc.uva.es:10324/788352025-12-16T20:00:53Zcom_10324_1151com_10324_931com_10324_894col_10324_1280

00925njm 22002777a 4500 dc Villaizán Vallelado, Mario author Salvatori, Matteo author Carro Martínez, Belén author Sánchez Esguevillas, Antonio Javier author 2025 Most Artificial Intelligence models require the information in the records to be used to be fully informed. These models require a policy for handling missing information. However, traditional policies have tried to fill in the missing information with known information. This approach is correct when the missing information is random and wrong when it is not. In this article we start with a review of the main policies employed, and analyse the consequences of elimination or imputation policies and argue that these policies are sometimes unwise. Andreu Catalá, Gonzalo Joya y Ignacio Rojas. IWANN 2025 18th International Work-Conference on Artificial Neural Networks. A Coruña: IWANN, 2025, p. 1-1. 979-13-8752213-1 https://uvadoc.uva.es/handle/10324/78835 Tackling Missing Data Head-On: Strategies to Mitigate Survival and Confirmation Bias