Pilot selection in the era of Virtual reality: algorithms for accurate and interpretable machine learning models
This addresses the need for cost-efficient pilot selection in the aviation industry, though it is incremental as it applies existing methods to a new domain with specific data.
The study tackled pilot selection by using machine learning and virtual reality to distinguish between pilots and novices, achieving an accuracy of 0.93, AUC of 0.96, and F1 of 0.93 with an SVM and MIC feature selection method.
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.