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Handling missing values in kernel methods with application to microbiology data

Author
Belanche, Ll.; Kobayashi, V.; Aluja, T.
Type of activity
Journal article
Journal
Neurocomputing
Date of publication
2014-10-02
Volume
141
First page
110
Last page
116
DOI
https://doi.org/10.1016/j.neucom.2014.01.047 Open in new window
Project funding
Artificial Intelligence-based decision tools for decision making support in oncology
Abstract
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...
Keywords
Binary variables, Fully conditional specification, Missing values, Multiple imputation, Multivariate imputation, Source tracking, Support vector machines
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
IMP - Information Modelling and Processing
SOCO - Soft Computing
inLab FIB

Participants