可扩展的航班取消预测与整体分布式KNN和功能选择预测
Ho Yin Kan1, Keith Chau2, Patrick Cheong-Iao Pang3
1Centre for Continuing Education, Macao Polytechnic University, Macao, China. hykan@mpu.edu.mo.
Scientific reports
|October 7, 2025
概括
这项研究引入了一种新的分布式集体学习方法,用于准确预测航班取消. 该方法显著提高了处理大型航空数据集的准确性和效率.
科学领域:
- 航空数据科学 航空数据科学
- 机器学习在运输中的应用
- 预测分析是一种预测分析.
背景情况:
- 准确的航班取消预测对于航空公司的财务健康和乘客满意度至关重要.
- 传统的方法难以应对航空大数据的数量和复杂性.
- 现有的预测模型缺乏可扩展性和最佳特征选择能力.
研究的目的:
- 开发一种创新的分布式集体学习方法,用于大规模的航班取消预测.
- 使用大数据环境提高预测准确性和计算效率.
- 解决航空行业传统预测方法的局限性.
主要方法:
- 利用分布式集体学习与MapReduce框架进行可扩展性.
- 实施分布式K-最近邻居 (DKNN) 模型,以实现高效的数据处理.
- 采用人工蜂群 (ABC) 算法从广泛的数据集中进行最佳特征选择.
主要成果:
- 与非分布式KNN模型相比,实现了超过25%的最小计算优势.
- 整体策略使预测准确度提高了3.42%,达到平均95.79%.
- 与以前的航班取消预测方法相比,其准确度提高了2.2%.
结论:
- 拟议的分布式集合方法有效地预测大数据环境中的航班取消情况.
- 该系统提供了显著的计算优势和增强的预测准确性.
- 这种方法为航空业的预测挑战提供了可扩展和准确的解决方案.
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