2026-08-07T19:21:03Zhttps://uvadoc.uva.es/oai/request

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

Villaizán Vallelado, Mario Salvatori, Matteo Carro Martínez, Belén Sánchez Esguevillas, Antonio Javier 2025-10-21T06:38:09Z 2025-10-21T06:38:09Z 2025 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 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. eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Attribution 4.0 Internacional Tackling Missing Data Head-On: Strategies to Mitigate Survival and Confirmation Bias info:eu-repo/semantics/conferenceObject