Title: Handling missing values in support vector machine classifiers
Authors: Pelckmans, Kristiaan ×
De Brabanter, Joseph
Suykens, Johan
De Moor, Bart #
Issue Date: Jun-2005
Publisher: Pergamon
Series Title: Neural Networks vol:18 issue:5-6 pages:684-692
Abstract: This paper discusses the task of learning a classifier from observed data containing missing values amongst the inputs which are missing completely at random(1). A non-parametric perspective is adopted by defining a modified risk taking into account the uncertainty of the predicted outputs when missing values are involved. It is shown that this approach generalizes the approach of mean imputation in the linear case and the resulting kernel machine reduces to the standard Support Vector Machine (SVM) when no input values are missing. Furthermore, the method is extended to the multivariate case of fitting additive models using componentwise kernel machines, and an efficient implementation is based on the Least Squares Support Vector Machine (LS-SVM) classifier formulation. (c) 2005 Elsevier Ltd. All rights reserved.
ISSN: 0893-6080
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Centre for Food and Microbial Technology
Technologiecluster ESAT Elektrotechnische Engineering
Electrical Engineering (ESAT) TC, Technology Campuses Ghent and Aalst
× corresponding author
# (joint) last author

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