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Variable selection in multivariate functional data classification

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Górecki T., Krzyśko M., Wołyński W.

Statistics in Transition new series

2019

vol. 20

issue 2

123-138

A new variable selection method is considered in the setting of classification with multivariate functional data (Ramsay and Silverman (2005)). The variable selection is a dimensionality reduction method which leads to replace the whole vector process, with a low-dimensional vector still giving a comparable classification error. Various classifiers appropriate for functional data are used. The proposed variable selection method is based on functional distance covariance (dCov) given by Székely and Rizzo (2009, 2012) and the Hilbert-Schmidt Independent Criterion (HSIC) given by Gretton et al. (2005). This method is a modification of the procedure given by Kong et al. (2015). The proposed methodology is illustrated with a real data example.

FAMILIES OF CLASSIFIERS – APPLICATION IN DATA

86%

Grzybowska U., Karwański M.

Metody Ilościowe w Badaniach Ekonomicznych

2014

vol. 15

issue 2

94-101

Economic description of firms and companies is based on a number of indicators. The indicators are related to each other and can be considered only in a specific context. Regression models allow for such approach. Unfortunately, the problems we deal with are usually nonlinear and the choice of relevant information is very difficult. The aim of the paper is to present a method of variable selection based on random forest and gradient boosting approach and its application to companies ranking in DEA method. The results will be compared with the ordering obtained using expert supported approach for variable selection in DEA.

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1 Metody Ilościowe w Badaniach Ekonomicznych

1 Statistics in Transition new series

1 Grzybowska U.

1 Górecki T.

1 Karwański M.

1 Krzyśko M.

1 Wołyński W.

1 2019

1 2014

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Variable selection in multivariate functional data classification

FAMILIES OF CLASSIFIERS – APPLICATION IN DATA