基于树的模型进行交叉验证,用于多目标学习
Yehuda Nissenbaum1, Amichai Painsky1
1Department of Industrial Engineering, Tel Aviv University, Tel Aviv, Israel.
Frontiers in artificial intelligence
|March 4, 2024
概括
这项研究引入了一种基于树的多目标学习 (MTL) 方法,利用目标相关性来提高预测准确度. 可解释方法使用交叉验证来识别和利用目标之间的关系,优于现有技术.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 多目标学习 (MTL) 同时预测多个结果,使用从线性模型到深度学习的各种方法.
- 现有的MTL技术往往难以有效地利用目标之间的相关性,可能会限制预测准确性和可解释性.
研究的目的:
- 引入一种新的,可解释的,基于树的多目标学习 (MTL) 方案.
- 利用多个目标之间的相关性来提高预测准确性.
- 通过使用交叉验证的分割标准,提供一种避免过度装配的方法.
主要方法:
- 提出了一种新的基于树的多目标学习 (MTL) 方法.
- 在每个树节点上使用交叉验证的分割标准来识别相关的目标.
- 该计划将目标相关性直接集成到树木建设过程中.
主要成果:
- 拟议的基于树的MTL方案显示了相对于替代方法的显著性能改进.
- 在合成和现实世界数据集上的实验验证实了该方法的有效性.
- 该方法成功地利用目标相关性来提高预测准确性,同时减轻过度拟合.
结论:
- 新型基于树的MTL方案提供了一个高度可解释和准确的方法,用于同时预测多个目标.
- 通过交叉验证分割利用目标相关性是提高MTL性能的有效策略.
- 公开可用的实施方便进一步研究和应用这种先进的MTL技术.
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