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

Damiano Lombardi 1 Fabien Raphel 1, *
* Corresponding author
1 COMMEDIA - COmputational Mathematics for bio-MEDIcal Applications
Inria de Paris, LJLL (UMR_7598) - Laboratoire Jacques-Louis Lions
Abstract : 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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Submitted on : Wednesday, October 13, 2021 - 10:10:31 PM
Last modification on : Tuesday, November 16, 2021 - 4:51:02 AM

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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, EDP Sciences, 2021, 55 (5), pp.2259-2291. ⟨10.1051/m2an/2021060⟩. ⟨hal-03377036v2⟩

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