Abstract
Incomplete data is a common drawback in many pattern classification applications. A classical way to deal with unknown values is missing data estimation. Most machine learning techniques work well with missing values, but they do not focus the missing data estimation to solve the classification task. This paper presents effective neural network approaches based on Multi-Task Learning (MTL) for pattern classification with missing inputs. These MTL networks are compared with representative procedures used for handling incomplete data on two well-known data sets. The experimental results show the superiority of our approaches with respect to alternative techniques.
This work is partially supported by Ministerio de Educación y Ciencia under grant TEC2006-13338/TCM, and by Consejería de Educación y Cultura de Murcia under grant 03122/PI/05.
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García-Laencina, P.J., Serrano, J., Figueiras-Vidal, A.R., Sancho-Gómez, JL. (2007). Multi-task Neural Networks for Dealing with Missing Inputs. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_28
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DOI: https://doi.org/10.1007/978-3-540-73053-8_28
Publisher Name: Springer, Berlin, Heidelberg
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