Summary of related work.

Flight delays pose substantial operational and economic challenges for airlines, directly affecting scheduling efficiency, resource allocation, and passenger satisfaction. Accurate prediction of arrival delays is therefore critical for optimizing airline operations and enhancing customer experience. This study systematically evaluates the predictive performance of six machine learning classifiers—Decision Tree, Random Forest, Support Vector Classifier (SVC), Logistic Regression, K-Nearest Neighbors (KNN), and Naive Bayes—on a comprehensive flight dataset, with particular attention to the challenges posed by class imbalance. To mitigate skewed class distributions, resampling techniques including Random Oversampling, Synthetic Minority Oversampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN) were applied to the training data. Model performance was rigorously assessed using stratified 10-fold cross-validation and further validated on a hold-out test set, employing multiple evaluation metrics: Accuracy, F1-score, Matthews Correlation Coefficient (MCC), and ROC-AUC. The results demonstrate that Random Forest combined with Random Oversampling and Decision Tree combined with SMOTE both achieved the highest predictive performance (accuracy 0.90, F1-score 0.90, MCC 0.73, ROC-AUC 0.87. Notably, simpler models such as Naive Bayes exhibited competitive results under balanced conditions, underscoring the continued relevance of probabilistic classifiers in certain operational contexts. These findings highlight the critical role of resampling strategies and rigorous cross-validation in developing reliable, high-performing predictive models for imbalanced flight delay datasets, offering actionable insights for both airline operations and data-driven decision-making.航班延误对航空公司而言构成了严峻的运营与经济挑战，直接影响排班效率、资源配置与旅客满意度。因此，精准预测航班到达延误，对于优化航空公司运营流程、提升旅客体验至关重要。本研究针对一套全面的航班数据集，系统评估了六种机器学习分类器——决策树（Decision Tree）、随机森林（Random Forest）、支持向量分类器（Support Vector Classifier, SVC）、逻辑回归（Logistic Regression）、K近邻（K-Nearest Neighbors, KNN）以及朴素贝叶斯（Naive Bayes）——的预测性能，并重点关注类别不平衡带来的建模挑战。为缓解类别分布失衡问题，本研究针对训练集应用了多种重采样技术，包括随机过采样（Random Oversampling）、合成少数类过采样技术（Synthetic Minority Oversampling Technique, SMOTE）以及自适应合成采样（Adaptive Synthetic Sampling, ADASYN）。本研究采用分层10折交叉验证对模型性能进行严格评估，并通过留出测试集做进一步验证，同时选用多项评价指标：准确率（Accuracy）、F1分数（F1-score）、马修斯相关系数（Matthews Correlation Coefficient, MCC）以及ROC-AUC。研究结果显示，结合随机过采样的随机森林模型，以及结合SMOTE的决策树模型均取得了最优预测性能（准确率0.90、F1分数0.90、马修斯相关系数0.73、ROC-AUC 0.87）。值得注意的是，朴素贝叶斯等简易模型在类别平衡的数据集下也展现出了颇具竞争力的性能，这表明概率分类器在部分运营场景中仍具备应用价值。本研究结果凸显了重采样策略与严格交叉验证，在构建针对不平衡航班延误数据集的可靠高性能预测模型时的关键作用，同时为航空公司运营与数据驱动决策提供了可落地的参考思路。