Detecting and approximating decision boundaries in low-dimensional spaces
- A method for detecting and approximating fault lines or surfaces, respectively, or decision curves in two and three dimensions with guaranteed accuracy is presented. Reformulated as a classification problem, our method starts from a set of scattered points along with the corresponding classification algorithm to construct a representation of a decision curve by points with prescribed maximal distance to the true decision curve. Hereby, our algorithm ensures that the representing point set covers the decision curve in its entire extent and features local refinement based on the geometric properties of the decision curve. We demonstrate applications of our method to problems related to the detection of faults, to multi-criteria decision aid and, in combination with Kirsch’s factorization method, to solving an inverse acoustic scattering problem. In all applications we considered in this work, our method requires significantly less pointwise classifications than previously employed algorithms.
Author: | Matthias GrajewskiORCiD, Andreas KleefeldORCiD |
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DOI: | https://doi.org/10.1007/s11075-023-01618-6 |
ISSN: | 1572-9265 |
Parent Title (English): | Numerical Algorithms |
Publisher: | Springer Science+Business Media |
Place of publication: | Dordrecht |
Document Type: | Article |
Language: | English |
Year of Completion: | 2023 |
Date of first Publication: | 2023/08/14 |
Tag: | Fault approximation; Fault detection; Inverse scattering problem; MCDA |
Volume: | 93 |
Issue: | 4 |
Length: | 35 Seiten |
Note: | Corresponding author: Matthias Grajewski |
Link: | https://doi.org/10.1007/s11075-023-01618-6 |
Institutes: | FH Aachen / Fachbereich Medizintechnik und Technomathematik |
open_access (DINI-Set): | open_access |
collections: | Verlag / Springer Science+Business Media |
Open Access / Hybrid | |
Geförderte OA-Publikationen / DEAL Springer | |
Licence (German): | Creative Commons - Namensnennung |