Conformal prediction for regression models with asymmetrically distributed errors: application to aircraft navigation during landing maneuver

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine Learning Pub Date : 2024-09-09 DOI:10.1007/s10994-024-06615-x
Solène Vilfroy, Lionel Bombrun, Thierry Urruty, Florence De Grancey, Jean-Philippe Lebrat, Philippe Carré
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Abstract

Semi-autonomous aircraft navigation is a high-risk domain where confidence on the prediction is required. For that, this paper introduces the use of conformal predictions strategies for regression problems. While standard approaches use an absolute nonconformity scores, we aim at introducing a signed version of the nonconformity scores. Experimental results on synthetic data have shown their interest for non-centered errors. Moreover, in order to reduce the width of the prediction interval, we introduce an optimization procedure which learn the optimal alpha risks for the lower and upper bounds of the interval. In practice, we show that a line search algorithm can be employed to solve it. Practically, this novel adaptive conformal prediction strategy has revealed to be well adapted for skew distributed errors. In addition, an extension of these conformal prediction strategies is introduced to incorporate numeric and categorical auxiliary variables describing the acquisition context. Based on a quantile regression model, they allow to maintain the coverage for each metadata value. All these strategies have then been applied on a real use case of runway localization from data acquired by an aircraft during landing maneuver. Extensive experiments on multiple airports have shown the interest of the proposed conformal prediction strategies, in particular for runways equipped with a very long ramp approach where asymmetric angular deviation error are observed.

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具有非对称分布误差的回归模型的共形预测:应用于着陆机动过程中的飞机导航
半自动飞机导航是一个高风险领域,需要对预测结果有信心。为此,本文介绍了使用保形预测策略来解决回归问题。标准方法使用绝对不符性分数,而我们的目标是引入符号版的不符性分数。在合成数据上的实验结果表明,它们对非中心误差很有意义。此外,为了减小预测区间的宽度,我们引入了一个优化程序,该程序可以学习区间上下限的最佳阿尔法风险。在实践中,我们证明可以采用线性搜索算法来解决这个问题。实践表明,这种新颖的自适应保形预测策略非常适合偏斜分布误差。此外,我们还对这些共形预测策略进行了扩展,以纳入描述采集环境的数字和分类辅助变量。基于量化回归模型,它们可以保持每个元数据值的覆盖范围。随后,所有这些策略都被应用到一个实际案例中,即根据飞机着陆时获取的数据进行跑道定位。在多个机场进行的大量实验表明,所提出的保形预测策略很有意义,特别是对于配备有很长坡道的跑道,在这种跑道上可以观察到不对称的角度偏差误差。
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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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