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Handling missing values in kernel methods with application to microbiology data
Kobayashi, Vladimer; Aluja Banet, Tomàs; Belanche Muñoz, Luis Antonio
Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa; Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. LIAM - Laboratori de Modelització i Anàlisi de la Informació; Universitat Politècnica de Catalunya. SOCO - Soft Computing
We discuss several approaches that make possible for kernel methods to deal with missing values. The first two are extended kernels able to handle missing values without data preprocessing methods. Another two methods are derived from a sophisticated multiple imputation technique involving logistic regression as local model learner. The performance of these approaches is compared using a binary data set that arises typically in microbiology (the microbial source tracking problem). Our results show that the kernel extensions demonstrate competitive performance in comparison with multiple imputation in terms of predictive accuracy. However, these results are achieved with a simpler and deterministic methodology and entail a much lower computational effort.
Peer Reviewed
Àrees temàtiques de la UPC::Matemàtiques i estadística::Anàlisi matemàtica
Integral equations
Anàlisi matemàtica
45H05 Miscellaneous special kernels
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
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