虚拟现实时代的飞行员选拔：面向精准可解释机器学习模型的算法
Pilot selection in the era of Virtual reality: algorithms for accurate and interpretable machine learning models
AI总结：
本研究针对航空业飞行员选拔需求，结合VR技术与SVM+MIC算法，基于眼动和飞行动力学数据实现了更优的飞行员技能分类效果，可用于飞行员选拔与培训。
摘要：
随着航空业的快速发展，行业对飞行机组人员的需求大幅增长。如何以高成本效益的方式选拔合适的飞行员，已成为一项重要研究问题。本研究从中国东方航空招募了23名飞行员，同时从清华大学社区招募了23名新手受试者。研究采用一种结合机器学习与虚拟现实技术的新方法，以区分具备不同飞行技能受试者的特征。结果显示，结合MIC（最大信息系数）特征选择方法的SVM（支持向量机）在所有指标上始终取得最高预测性能，准确率达0.93、AUC达0.96、F1值达0.93，性能优于另外4种分类器算法与2种其他特征选择方法。从特征选择方法的角度来看，MIC方法能够筛选出与采样标签存在非线性关系的特征，而非采用简单过滤剔除的方式。我们提出的SVM+MIC算法新实现方案性能优于所有现有飞行员选拔算法，并且可能是首个基于眼动追踪与飞行动力学数据的相关实现方案。本研究的VR仿真平台与算法可应用于飞行员选拔与培训场景。
英文摘要：
With the rapid growth of the aviation industry, there is a need for a large number of flight crew. How to select the right pilots in a cost-efficient manner has become an important research question. In the current study, twenty-three pilots were recruited from China Eastern Airlines, and 23 novices were from the community of Tsinghua University. A novel approach incorporating machine learning and virtual reality technology was applied to distinguish features between these participants with different flight skills. Results indicate that SVM with the MIC feature selection method consistently achieved the highest prediction performance on all metrics with an Accuracy of 0.93, an AUC of 0.96, and an F1 of 0.93, which outperforms four other classifier algorithms and two other feature selection methods. From the perspective of feature selection methods, the MIC method can select features with a nonlinear relationship to sampling labels, instead of a simple filter-out. Our new implementation of the SVM + MIC algorithm outperforms all existing pilot selection algorithms and perhaps provides the first implementation based on eye tracking and flight dynamics data. This study's VR simulation platforms and algorithms can be used for pilot selection and training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
