Author:
Sieberichs Christian,Geerkens Simon,Braun Alexander,Waschulzik Thomas
Abstract
AbstractWith the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important. In this paper, we present a novel approach for the assurance of data quality. For this purpose, the mathematical basics are first discussed and the approach is presented using multiple examples. This results in the detection of data points with potentially harmful properties for the use in safety-critical systems.
Funder
Hochschule Düsseldorf University of Applied Sciences
Publisher
Springer Science and Business Media LLC
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