Regression SVM for incomplete data

• Autorzy: Struski Ł , Śmieja M. , Zieliński B. , Tabor J.

Identyfikatory

DOI

10.4467/20838476SI.17.001.6807

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

regression SVM; incomplete data; missing attributes; RBF kernel

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2017
• Tom: Vol. 26
• Strony: 23--35
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Struski Ł

• Faculty of Mathematics and Computer Science Lojasiewicza 6, 30-348 Kraków

autor

Śmieja M.

• Faculty of Mathematics and Computer Science Lojasiewicza 6, 30-348 Kraków

autor

Zieliński B.

• Faculty of Mathematics and Computer Science Lojasiewicza 6, 30-348 Kraków

autor

Tabor J.

• Faculty of Mathematics and Computer Science Lojasiewicza 6, 30-348 Kraków

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).