%0 Journal Article 
%A García Laencina, Pedro José
%A Sancho Gómez, José Luis
%T Red neural multitarea para problemas de decisión con información incompleta
%D 2006
%U http://hdl.handle.net/10317/1354
%X Missing data is a common problem that appears
in many real applications. Handling missing data is a essential
requirement for pattern classification because inappropriate
treatment of missing data may cause large errors or false results
on classification. A classical approach is to estimate and fill
the missing values. Up to now, proposed methods are efficient
but they do not focus the missing data estimation to pattern
classification. In this work, we propose a novel neural network
that uses the advantages of Multitask Learning (MTL) to classify
patterns with incomplete data. A MTL neural network learns
at the same time the classification task and the different task
associated to incomplete features. Missing data estimation is
guided and oriented by the classification task during the MTL
process. Obtained results based on real and artificial classification
problems demonstrate the advantages of the proposed algorithm.
%K Ingeniería Telemática
%K Red neural multitarea
%K Aprendizaje multitarea
%K Clasificación de patrones
%K Información incompleta
%K Red neuronal artificial
%K Neural network multitask
%K Multitask learning
%K Pattern classification
%K Incomplete information
%K Artificial neural networks
%~ GOEDOC, SUB GOETTINGEN