在先前概率转移下高效的数据集成
Ming-Yueh Huang1, Jing Qin2, Chiung-Yu Huang3
1Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan.
Biometrics
|May 20, 2024
概括
本研究引入了一种新的算法来处理数据集转移,特别是先前概率转移,通过结合来自不同人群的数据. 该方法适用于离散和连续结果,并提高机器学习中的模型准确性.
科学领域:
- 机器学习 机器学习
- 统计建模 统计建模
- 数据科学数据科学数据科学
背景情况:
- 监督学习假定来自单一人群的数据,但整合来自不同人群的数据会导致数据集的转移.
- 前期概率转移是一种数据集转移,结果分布不同,但特征条件结果分布保持不变.
研究的目的:
- 为先前概率转移提出一个高效的估计算法,该算法结合了来自多个数据源的信息.
- 开发一种适应离散和连续结果的方法,与现有方法不同,仅限于离散结果.
- 引入一种新的半参数概率测试,用于验证先前的概率转移假设.
主要方法:
- 一种估计算法,将多个来源的信息结合起来,用于预先的概率转移.
- 使用适应性最小绝对收缩和选择操作员 (LASSO) 对高维数据的处罚来选择变量.
- 一个半参数概率测试,将零条件密度嵌入到尼曼平滑替代品中.
主要成果:
- 拟议的算法有效地将来自多个来源的信息结合起来,处理离散和连续结果.
- 使用自适应LASSO进行变量选择,可获得高维共变量的Oracle属性的高效估计.
- 概率比率测试有效验证了先前的概率转移假设.
结论:
- 开发的方法有效地解决了机器学习中的先前概率转移.
- 该方法提供了一个强大的工具,用于整合来自不同人群的数据,增强模型的通用性.
- 提出的技术为管理数据集转移挑战的工具包提供了有价值的补充.
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