Active Output Selection Strategies for Multiple Learning Regression Models

TL;DR

This paper introduces a new active output selection strategy for multiple regression models that reduces data collection efforts by up to 30%, outperforming existing methods in noisy environments.

Contribution

The paper proposes a novel active output selection method that actively learns multiple outputs simultaneously, tailored for calibration tasks.

Findings

Reduces sample points by up to 30% compared to sequential design.

Outperforms existing active learning strategies in benchmark tests.

Shows promising results but needs robustness improvements for noisy data.

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 research will focus on improving the algorithm and applying it to a real-world example.