TY  - JOUR
A1  - Grajewski, Matthias
A1  - Kleefeld, Andreas
T1  - Detecting and approximating decision boundaries in low-dimensional spaces
JF  - Numerical Algorithms
N2  - 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.
KW  - MCDA
KW  - Inverse scattering problem
KW  - Fault approximation
KW  - Fault detection
Y1  - 2023
SN  - 1572-9265
N1  - Corresponding author: Matthias Grajewski
VL  - 93
IS  - 4
PB  - Springer Science+Business Media
CY  - Dordrecht
ER  -