TY - JOUR 
A1 - García Laencina, Pedro José
AU - Sancho Gómez, José Luis
AU - Figueiras Vidal, Anibal R.
T1 - Pattern classification with missing values using multitask learning
Y1 - 2006
UR - http://hdl.handle.net/10317/707
AB - In many real-life applications it is important
to know how to deal with missing data (incomplete feature
vectors). The ability of handling missing data has become a
fundamental requirement for pattern classification because inappropriate
treatment of missing data may cause large errors or
false results on classification. A novel effective neural network
is proposed to handle missing values in incomplete patterns
with Multitask Learning (MTL). In our approach, a MTL
neural network learns in parallel the classification task and
the different tasks associated to incomplete features. During the
MTL process, missing values are estimated or imputed. Missing
data imputation is guided and oriented by the classification task,
i.e., imputed values are those that contribute to improve the
learning. We prove the robustness of this MTL neural network
for handling missing values in classification problems from UCI
database.
KW - Teoría de la Señal y las Comunicaciones
KW - Aprendizaje multitarea
KW - Red neuronal
KW - Red MTL
KW - Clasificación de patrones
KW - Learning multitasking
KW - Neural network
KW - MTL network
KW - Pattern classification
LA - eng
PB - Institute of Electrical and Electronics Engineers ( IEEE )
ER -