Pattern classification with missing values using multitask learning
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Teoría de la Señal y las ComunicacionesPatrocinadores
This work will stimulate future works in many directions. Some of them are using different error functions (crossentropy error in discrete tasks, and sum-of-squares error in continuous tasks), adding an EM-model to probability density estimation into the proposed MTL scheme, setting the number of neurons in each subnetwork dynamically using constructive learning, an extensive comparison with other imputation methods, to use this procedure in regression problems, and extending the proposed method to different machines, e.g., Support Vector Machines (SVM).Fecha de publicación
2006-07Editorial
Institute of Electrical and Electronics Engineers ( IEEE )Cita bibliográfica
GARCÍA LAENCINA, Pedro José; SANCHO GÓMEZ, José Luis y FIGUEIRAS VIDAL, Anibal R. Pattern classification with missing values using multitask learning. En: International Joint Conference on Neural Networks (2006: Vancouver, BC, Canadá) 2006 IEEE World Congress on Computational Intelligence. 2006 International Joint Conference on Neural Networks. IJCNN´06, July 16-21, 2006. Vancouver, BC (Canadá), 2006. Pp. 3594-3601.Palabras clave
Aprendizaje multitareaRed neuronal
Red MTL
Clasificación de patrones
Learning multitasking
Neural network
MTL network
Pattern classification
Resumen
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.
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