Volume 55, Number 5, September-October 2021
|Page(s)||2259 - 2291|
|Published online||13 October 2021|
A method to enrich experimental datasets by means of numerical simulations in view of classification tasks
Inria, 75012 Paris, France
2 NOTOCORD® Part of Instem, 78230 Le Pecq, France
* Corresponding author: email@example.com
Accepted: 23 September 2021
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.
Mathematics Subject Classification: 60B10 / 68T05
Key words: Classification / Hausdorff distance / nearest neighbors
© The authors. Published by EDP Sciences, SMAI 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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