Active Output Selection Strategies for Multiple Learning Regression Models

Adrian Prochaska, Julien Pillas, Bernard Bäker

Abstract

Active learning shows promise to decrease test bench time for model-based drivability calibration. This paper presents a new strategy for active output selection, which suits the needs of calibration tasks. The strategy is actively learning multiple outputs in the same input space. It chooses the output model with the highest cross-validation error as leading. The presented method is applied to three different toy examples with noise in a real world range and to a benchmark dataset. The results are analyzed and compared to other existing strategies. In a best case scenario, the presented strategy is able to decrease the number of points by up to 30 % compared to a sequential space-filling design while outperforming other existing active learning strategies. The results are promising but also show that the algorithm has to be improved to increase robustness for noisy environments. Further reasearch will focus on improving the algorithm and applying it to a real-world example.

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Paper Citation


in Harvard Style

Prochaska A., Pillas J. and Bäker B. (2021). Active Output Selection Strategies for Multiple Learning Regression Models.In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 150-157. DOI: 10.5220/0010181501500157


in Bibtex Style

@conference{icpram21,
author={Adrian Prochaska and Julien Pillas and Bernard Bäker},
title={Active Output Selection Strategies for Multiple Learning Regression Models},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2021},
pages={150-157},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010181501500157},
isbn={978-989-758-486-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Active Output Selection Strategies for Multiple Learning Regression Models
SN - 978-989-758-486-2
AU - Prochaska A.
AU - Pillas J.
AU - Bäker B.
PY - 2021
SP - 150
EP - 157
DO - 10.5220/0010181501500157