RT info:eu-repo/semantics/conferenceObject
T1 Tackling Missing Data Head-On: Strategies to Mitigate Survival and Confirmation Bias
A1 Villaizán Vallelado, Mario
A1 Salvatori, Matteo
A1 Carro Martínez, Belén
A1 Sánchez Esguevillas, Antonio Javier
AB 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.
PB IWANN, A coruña
SN 979-13-8752213-1
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/78835
UL https://uvadoc.uva.es/handle/10324/78835
LA eng
NO Andreu Catalá, Gonzalo Joya y Ignacio Rojas. IWANN 2025 18th International Work-Conference on Artificial Neural Networks. A Coruña: IWANN, 2025, p. 1-1.
NO Producción Científica
DS UVaDOC
RD 11-nov-2025