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DCC:一个无模型的框架来评估数据集质量
1Department of Polymer Materials and Engineering, College of Materials and Metallurgy, Guizhou University, Guiyang 550025, P.R. China.
Journal of chemical information and modeling
|December 9, 2025
概括
我们介绍了数据相关性收 (DCC),这是评估数据集质量的新框架. DCC量化了扰动下的数据稳定性,为评估数据完整性和代表性提供了传统方法的计算效率高的替代方案.
科学领域:
- 数据科学数据科学数据科学
- 材料科学 材料科学 材料科学
- 统计建模 统计建模
背景情况:
- 评估数据集质量对于可靠的分析和模型性能至关重要.
- 现有的数据质量评估方法往往是计算密集型和模型依赖的.
- 需要一个理论上的基础和广泛适用的框架来评估数据的完整性和代表性.
研究的目的:
- 为评估数据集质量提出数据相关性趋同 (DCC) 框架.
- 为传统的计算密集型和依赖模型的方法提供替代方案.
- 量化数据集在扰动下的稳定性,反映完整性和代表性.
主要方法:
- DCC集成了多个相关函数来量化数值相关性和分布相似性.
- 该框架假设高质量的数据集在扰乱下表现出稳定的相关性模式.
- 用假设和基准数据集来验证DCC框架的有效性.
主要成果:
- 最低的DCC值在10-20%的线性相关性中观察到,随着更具决定性的相关性而增加.
- DCC值有效地预测机器学习模型的性能指标 (例如,精度,R平方) 和特征重要性 (SHAP值).
- 通过捕捉固有的相关性模式,DCC可以有效地压缩数据集.
结论:
- DCC框架为数据集质量评估提供了一个理论上有根据的,广泛适用的和可扩展的方法.
- DCC提供了关于数据完整性,代表性和潜在偏差的见解.
- 这种方法可以为科学研究和机器学习应用提供更好的数据注释和选择.
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