LLpowershap:基于物流损失的自动化Shapley值特征选择方法.
Iqbal Madakkatel1,2, Elina Hyppönen3,4
1Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, 5001, South Australia, Australia. iqbal.madakkatel@unisa.edu.au.
一种新的特征选择方法LLpowershap能够识别出更具信息性的特征,并且噪音更低. 该方法在真实数据上显示出优越或可比的预测性能,在测试方法中排名最佳.
科学领域:
- 机器学习 机器学习
- 生物信息学是一种生物信息学.
- 数据科学数据科学数据科学
背景情况:
- 沙普利值对于解释复杂的机器学习模型至关重要,有助于调试,公平分析和功能选择.
- 像 powershap 这样的现有方法利用预测性的 Shapley 值和 p 值来进行特征选择.
- 沙普利值确保了特征贡献的公平分配,考虑到非线性和相互作用.
研究的目的:
- 介绍LLpowershap,一种使用基于损失的Shapley值的新型特征选择方法.
- 提高信息特征的识别,同时最大限度地减少噪音.
- 改进p值和功率的计算,用于特征选择和代估计.
主要方法:
- 使用基于损失的Shapley值来进行特征选择.
- 包括增强的p值和功率计算.
- 通过对真实世界数据集的模拟和基准测试进行评估.
主要成果:
- 与现有方法相比,LLpowershap识别了更多的信息特征和更少的噪音特征.
- 与其他基于Shapley和过方法相比,显示出高或可比的预测性能.
- 在七种测试的特征选择方法中获得最高平均排名.
结论:
- LLpowershap是一种有效的包装特征选择方法.
- 适用于大规模生物医学数据集和其他复杂数据环境中的特征选择.
- 提供了一种强大的方法来识别机器学习模型中的关键特征.
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