1732-3916 Regression SVM for Incomplete Data Struski Łukasz author Śmieja Marek author Zieliński Bartosz author Tabor Jacek author Uniwersytet Jagielloński w Krakowie, Polska, ul. Gołębia 24, 31-007 Kraków Faculty of Mathematics and Computer Science, Jagiellonian University ul. Łojasiewicza 6, 30-348 Kraków, Poland Correspondence to: Łukasz Struski lukasz.struski@im.uj.edu.pl Correspondence to: Marek Śmieja marek.smiejag@ii.uj.edu.pl Correspondence to: Bartosz Zieliński Correspondence to: Jacek Tabor jacek.tabor@uj.edu.pl 16 02 2018 Volume 26 2017 23 35 Copyright © 2018 2018 <p style="text-align: left;">The use of machine learning methods in the case of incomplete data is an important task in many scientific fields, like medicine, biology, or face recognition. Typically, missing values are substituted with artificial values that are estimated from the known samples, and the classical machine learning algorithms are applied. Although this methodology is very common, it produces less informative data, because artificially generated values are treated in the same way as the known ones. In this paper, we consider a probabilistic representation of missing data, where each vector is identified with a Gaussian probability density function, modeling the uncertainty of absent attributes. This representation allows to construct an analogue of RBF kernel for incomplete data. We show that such a kernel can be successfully used in regression SVM. Experimental results confirm that our approach capture relevant information that is not captured by traditional imputation methods.</p> 1 [1] Little R.J., D’Agostino R., Cohen M.L., Dickersin K., Emerson S.S., Farrar J.T., Frangakis C., Hogan J.W., Molenberghs G., Murphy S.A., et al., The prevention and treatment of missing data in clinical trials. 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