用深度学习区分民航、军用和私人飞行记录，BLSTM表现最佳。
Classification of Operational Records in Aviation Using Deep Learning Approaches
- 采用BLSTM、LSTM等模型处理飞行记录序列数据
- BLSTM准确率达72%，对三类飞行的分类最稳定
- 适合关注航空安全与序列数据分析的研究者
保障航空业安全至关重要，即使微小异常也可能导致严重后果。本研究评估了四种深度学习模型（双向LSTM、CNN、LSTM、简单RNN）在使用Socrata航空数据集（4,864条记录）进行民航、军用和私人飞行类别多分类任务中的表现。通过分类报告、混淆矩阵、准确率及验证损失/精度曲线进行评估。其中，双向LSTM达到最高总体准确率72%，在稳定性与平衡分类方面表现最优；LSTM紧随其后，准确率71%，在民航类别召回率突出。CNN和简单RNN准确率分别为67%和69%，在私人类别存在明显误判。尽管模型在捕捉序列依赖与复杂分类上具优势，但所有模型均受类别不平衡影响，尤其在预测军用与私人类别时表现不佳。通过数据增强、高级特征工程与集成学习可进一步提升分类精度与鲁棒性。本研究强调为特定领域任务选择合适架构的重要性。
原文摘要 · Abstract (English)
Ensuring safety in the aviation industry is critical, even minor anomalies can lead to severe consequences. This study evaluates the performance of four different models for DP (deep learning), including: Bidirectional Long Short-Term Memory (BLSTM), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Simple Recurrent Neural Networks (sRNN), on a multi-class classification task involving Commercial, Military, and Private categories using the Socrata aviation dataset of 4,864 records. The models were assessed using a classification report, confusion matrix analysis, accuracy metrics, validation loss and accuracy curves. Among the models, BLSTM achieved the highest overall accuracy of 72%, demonstrating superior performance in stability and balanced classification, while LSTM followed closely with 71%, excelling in recall for the Commercial class. CNN and sRNN exhibited lower accuracies of 67% and 69%, with significant misclassifications in the Private class. While the results highlight the strengths of BLSTM and LSTM in handling sequential dependencies and complex classification tasks, all models faced challenges with class imbalance, particularly in predicting the Military and Private categories. Addressing these limitations through data augmentation, advanced feature engineering, and ensemble learning techniques could enhance classification accuracy and robustness. This study underscores the importance of selecting appropriate architectures for domain specific tasks
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