Classifying patterns with missing values using Multi-Task Learning perceptrons

Authors:
Pedro J. GarcíA-Laencina;José-Luis Sancho-GóMez;AníBal R. Figueiras-Vidal
Affiliations:
Centro Universitario de la Defensa de San Javier (University Centre of Defence at the Spanish Air Force Academy), MDE-UPCT, Calle Coronel Lopez Peña, s/n, 30720 Santiago de la Ribera, Murcia, ...;Universidad Politécnica de Cartagena, Department of Information and Communications Technologies, Plaza del Hospital 1, 30202 Cartagena, Murcia, Spain;Universidad Carlos III de Madrid, Department of Signal Theory and Communications, Avda. de la Universidad 30, 28911 Leganés, Madrid, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

Datasets with missing values are frequent in real-world classification problems. It seems obvious that imputation of missing values can be considered as a series of secondary tasks, while classification is the main purpose of any machine dealing with these datasets. Consequently, Multi-Task Learning (MTL) schemes offer an interesting alternative approach to solve missing data problems. In this paper, we propose an MTL-based method for training and operating a modified Multi-Layer Perceptron (MLP) architecture to work in incomplete data contexts. The proposed approach achieves a balance between both classification and imputation by exploiting the advantages of MTL. Extensive experimental comparisons with well-known imputation algorithms show that this approach provides excellent results. The method is never worse than the traditional algorithms - an important robustness property - and, also, it clearly outperforms them in several problems.