Active Output Selection Strategies for Multiple Learning Regression Models
This work addresses the problem of reducing data collection effort for engineers performing drivability calibration, offering an incremental improvement in active learning strategies.
This paper introduces a new active output selection strategy for multiple learning regression models, aiming to reduce test bench time in model-based drivability calibration. The strategy selects the output model with the highest cross-validation error as leading and, in a best-case scenario, reduces the number of data points by up to 30% compared to sequential space-filling designs while outperforming other active learning strategies.
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 research will focus on improving the algorithm and applying it to a real-world example.