Associated kernel discriminant analysis for multivariate mixed data

Authors

  • Some Matthieu University of Franche-Comté, LMB Besançon 16 route de Gray, 25030 Besançon cedex
  • Célestin C. Kokonendji University of Franche-Comté, LMB Besançon 16 route de Gray, 25030 Besançon cedex
  • Mona Ibrahim FCLAB/Femto-ST Belfort rue Thierry Mieg, F-90010 Belfort

DOI:

https://doi.org/10.1285/i20705948v9n2p385

Keywords:

Bandwidth matrix, non-classical kernel, profile cross-validation

Abstract

Associated kernels have been introduced to improve the classical (symmetric) continuous kernels for smoothing any functional on several kinds of supports such as bounded continuous and discrete sets. In this paper, an associated kernel for discriminant analysis with multivariate mixed variables is proposed. These variables are of three types: continuous, categorical and
count. The method consists of using a product of adapted univariate associated kernels and an estimate of the misclassication rate. A new prole version cross-validation procedure of bandwidth matrices selection is introduced for multivariate mixed data, while a classical cross-validation is used for homogeneous data sets having the same reference measures. Simulations and validation results show the relevance of the proposed method. The method has been validated on real coronary heart disease data in comparison to the classical kernel discriminant analysis.

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Published

14-10-2016

Issue

Vol. 9 No. 2 (2016): Electronic Journal of Applied Statistical Analysis

Section

Original Paper

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