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Article Dans Une Revue ESAIM: Mathematical Modelling and Numerical Analysis Année : 2021

A method to enrich experimental datasets by means of numerical simulations in view of classification tasks

Résumé

Classification tasks are frequent in many applications in science and engineering. A wide variety of statistical learning methods exist to deal with these problems. However, in many industrial applications, the number of available samples to train and construct a classifier is scarce and this has an impact on the classifications performances. In this work, we consider the case in which some a priori information on the system is available in form of a mathematical model. In particular, a set of numerical simulations of the system can be integrated to the experimental dataset. The main question we address is how to integrate them systematically in order to improve the classification performances. The method proposed is based on Nearest Neighbours and on the notion of Hausdorff distance between sets. Some theoretical results and several numerical studies are proposed.
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Origine : Publication financée par une institution

Dates et versions

hal-03377036 , version 1 (31-03-2021)
hal-03377036 , version 2 (27-09-2021)
hal-03377036 , version 3 (13-10-2021)

Identifiants

Citer

Damiano Lombardi, Fabien Raphel. A method to enrich experimental datasets by means of numerical simulations in view of classification tasks. ESAIM: Mathematical Modelling and Numerical Analysis, 2021, 55 (5), pp.2259-2291. ⟨10.1051/m2an/2021060⟩. ⟨hal-03377036v3⟩
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