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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data. John Wiley & Sons, Hoboken (2002)
Sharpe, P.K., Solly, R.J.: Dealing with missing values in neural network-based diagnostic systems. Neural Computing and Applications 3(2), 73–77 (1995)
Batista, G.E.A.P.A., Monard, M.C.: A study of k-nearest neighbour as an imputation method. In: Abraham, A., del Solar, J.R., Köppen, M. (eds.) HIS. Frontiers in Artificial Intelligence and Applications, vol. 87, pp. 251–260. IOS Press, Amsterdam (2002)
Ghahramani, Z., Jordan, M.I.: Supervised learning from incomplete data via an EM approach. In: Cowan, J.D., Tesauro, G., Alspector, J. (eds.) Advances in NIPS, vol. 6, pp. 120–127. Morgan Kaufmann, San Francisco (1994)
García-Laencina, P.J., Sancho-Gómez, J.-L., Figueiras-Vidal, A.R.: Pattern classification with missing values using multitask learning. In: IJCNN, Vancouver, BC, Canada, 16-21 July 2006, pp. 3594–3601. IEEE Computer Society Press, Los Alamitos (2006)
García-Laencina, P.J., Figueiras-Vidal, A.R., Serrano-García, J., Sancho-Gómez, J.L.: Exploiting multitask learning schemes using private subnetworks. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 233–240. Springer, Heidelberg (2005)
Caruana, R.: Multitask Learning. PhD thesis, Carnegie Mellon University (1997)
Silver, D.L.: Selective Transfer of Neural Network Task Knowledge. PhD thesis, University of Western Ontario (2000)
Evgeniou, T., Micchelli, C., Pontil, M.: Learning multiple tasks with kernel methods. Journal of Machine Learning Research 6, 615–637 (2005), http://jmlr.csail.mit.edu/papers/volume6/evgeniou05a
Newman, D.J., Hettich, S., Blake, C.L., Merz, C.: UCI repository of machine learning databases (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-73053-8_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73052-1
Online ISBN: 978-3-540-73053-8
eBook Packages: Computer ScienceComputer Science (R0)