%0 Journal Article 
%A García Laencina, Pedro José
%A Sancho Gómez, José Luis
%T Imputación de datos incompletos y clasificación de patrones mediante aprendizaje multitarea
%D 2005
%U http://hdl.handle.net/10317/703
%X Almost all research on supervised learning is based
on the assumption that training data are completely observable,
but it is not a common situation because real world databases are
rarely complete. The ability of handling missing data has become
a fundamental requirement for machine learning. Up to now,
proposed methods consider the problem as two separated tasks,
main task and imputation task, and solve them separately (Single
Task Learning, STL). In this paper, a new effective method is
proposed to handle missing features in incomplete databases with
Multitask Learning (MTL). This approach uses the imputation
task as extra task and learning in parallel with the main task.
Thus, imputation is guided and oriented by the learning process,
i.e., imputed values are those that contribute to improve the
learning. In this paper we use the advantages of MTL to handling
missing data and analyze its robustness for handling different
missing variables in real an artificial data sets.
%K Teoría de la Señal y las Comunicaciones
%K Aprendizaje multitarea
%K Redes neuronales artificiales
%K Esquema estándar MTL
%K Subredes
%~ GOEDOC, SUB GOETTINGEN