3 research outputs found
Pattern classification with missing values using multitask learning
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.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)
Combinación del aprendizaje multiarea y del algoritmo en problemas de clasificación con datos incompletos
Escuela Técnica Superior de IngenierÃa de Telecomunicació