使用基于树的神经网络预测酒店预订取消情况
1Wuhan Polytechnic, Wuhan City, Hubei Province, China.
PeerJ. Computer science
|December 9, 2024
概括
预测酒店预订取消对于收入管理至关重要. 一个新的基于树的神经网络 (TNN) 模型显著提高了预测准确性,为酒店业提供了有前途的解决方案.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 酒店取消服务会破坏收入管理的准确性.
- 计算方面的进步使预测建模能够用于降低风险.
- 现有的模型需要在现实世界中进行测试,并集成到决策支持系统中.
研究的目的:
- 开发和评估一种用于预测酒店预订取消的新计算方法.
- 评估预测模型在酒店决策支持系统中的整合.
- 分析预测模型对需求管理策略的影响.
主要方法:
- 引入基于树的神经网络 (TNN).
- 该TNN结合了基于树的学习算法与前神经网络.
- 在两个基准数据集上进行模型测试.
主要成果:
- TNN模型显示了显著改善的预测能力.
- 与传统的以树为基础的模型相比,性能优越.
- 该TNN的表现超过了基线的人工神经网络.
结论:
- 基于树的神经网络显示出预测酒店预订取消的前景.
- TNN模型为表格数据提供了一种可行的计算方法.
- 建议在现实世界条件下进一步验证.
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