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Updated: Jan 9, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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航班延误预测:评估机器学习算法以提高准确性.

Sarah Ahmed A AlBassam1, Dhafir N AlShahrani1

  • 1Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.

PloS one
|December 8, 2025
PubMed
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此摘要是机器生成的。

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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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准确的航班延误预测对航空公司至关重要. 机器学习模型,特别是具有重新采样技术的随机森林和决策树,在预测延迟方面表现出很高的表现,即使数据不平衡.

科学领域:

  • 航空运营研究 航空运营研究
  • 机器学习应用 机器学习应用
  • 数据科学数据科学数据科学

背景情况:

  • 航班延误严重影响航空公司的效率,资源管理和乘客满意度.
  • 准确预测航班抵达延误对于运营优化和改善客户体验至关重要.
  • 航班延误数据集中的类失衡对预测建模提出了重大挑战.

研究的目的:

  • 系统地评估六个机器学习分类器的预测性能,用于预测航班延误.
  • 研究各种重新采样技术在缓解阶级不平衡方面的有效性.
  • 为了确定最佳的模型和重新采样组合,以准确预测航班延误.

主要方法:

  • 评估了六个机器学习分类器:决策树,随机森林,支持向量分类器 (SVC),物流回归,K-最近邻居 (KNN) 和天真贝叶斯.
  • 应用了包括随机过量抽样,合成少数人过量抽样技术 (SMOTE) 和自适应合成抽样 (ADASYN) 在内的重新抽样技术来解决阶级不平衡问题.
  • 使用分层十倍交叉验证和持久测试组进行严格的性能评估,使用准确性,F1得分,MCC和ROC-AUC等指标.

主要成果:

  • 随机过量采样的随机森林和使用SMOTE的决策树实现了最高的预测性能 (精度为0.90,F1得分为0.90,MCC为0.73,ROC-AUC为0.87).

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  • 再抽样策略显著改善了对失衡航班延误数据集的模型性能.
  • 像天真贝叶斯这样的更简单的模型在数据平衡时显示出竞争性结果,表明它们的持续效用.

    • 结论:

    • 重复采样技术对于开发可靠的预测模型来处理失衡的航班延误数据至关重要.
    • 集合方法如随机森林和基于树的方法如决策树,当与适当的重新采样相结合时,可提供卓越的预测准确性.
    • 该研究为航空公司提供了可操作的见解,通过数据驱动的延误预测来提高运营效率和决策.