Home // ALLDATA 2023, The Ninth International Conference on Big Data, Small Data, Linked Data and Open Data // View article
Authors:
Xukuan Xu
Felix Conrad
Andreas Gronbach
Michael Möckel
Keywords: Small-data; Process uncertainty; Design Of Experiments (DOE); Machine learning.
Abstract:
As the algorithms mature, the bottleneck in applying Machine Learning (ML) to process analysis, monitoring and control is often caused by the availability of suitable data and the cost of data acquisition. For many ML projects, datasets have been collected independently of subsequent analysis. In industrial production, data acquisition and coverage of possible process uncertainties pose challenges to the preparation of suitable datasets. This article discusses dataset generation for ML from scratch under the constraint of limited resources with process uncertainties. A new approach towards an adapted Design Of Experiments (DOE) is proposed with the aim of sampling data more efficiently. In this way, we contribute to the challenge of preparing datasets for ML applications.
Pages: 35 to 38
Copyright: Copyright (c) IARIA, 2023
Publication date: April 24, 2023
Published in: conference
ISSN: 2519-8386
ISBN: 978-1-68558-041-4
Location: Venice, Italy
Dates: from April 24, 2023 to April 28, 2023