HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

No abstract

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Recently, there has been a growing interest for mixed categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies. Among the recently developed methods, we could cite: GP models built using continuous relaxation of the variables, Gower distance based models or GP models derived from direct estimation of the correlation matrix.In this paper, we present a kernel-based approach that extends continuous Gaussian kernels to handle mixed-categorical variables. The proposed kernel leads to a GP surrogate that generalizes continuous relaxation and Gower distance based GP models. The good potential of the proposed framework is shown on analytical mixed-categorical variables test cases. On different settings, our proposed GP models is as accurate as the state-of-the-art GP models.

No abstract