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Handling missing values in kernel methods with application to microbiology data

Autor
Belanche, Ll.; Kobayashi, V.; Aluja, T.
Tipus d'activitat
Article en revista
Revista
Neurocomputing
Data de publicació
2014-10-02
Volum
141
Pàgina inicial
110
Pàgina final
116
DOI
https://doi.org/10.1016/j.neucom.2014.01.047 Obrir en finestra nova
Projecte finançador
AIDTUMOUR: HERRAMIENTAS BASADAS EN METODOS DE INTELIGENCIA ARTIFICIAL PARA EL APOYO A LA DECISION EN
Resum
We discuss several approaches that make possible for kernel methods to deal with missing values for binary variables. 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 pro...
Paraules clau
Binary variables, Fully conditional specification, Missing values, Multiple imputation, Multivariate imputation, Source tracking, Support vector machines
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
IMP - Information Modeling and Processing
SOCO - Soft Computing

Participants