在学习游戏中的实验随机化的基于ML的验证
1Department of Computer Science, College of Informatics, National Chengchi University, Taipei, Taiwan.
Frontiers in artificial intelligence
|November 17, 2025
概括
机器学习模型可以验证实验中的参与者随机化. 监督模型达到87%的准确性,提供了一种用于检测研究中的分配偏差的新方法.
科学领域:
- 实验研究方法的实验研究方法.
- 计算统计的计算统计.
- 在医疗保健中的数据科学.
背景情况:
- 随机化对于实验有效性至关重要,但可能会受到损害.
- 现有的随机验证方法是有限的.
- 机器学习 (ML) 提供了增强验证的潜力.
研究的目的:
- 引入和评估机器学习模型,作为验证参与者随机化的补充工具.
- 评估监督和无监督ML模型在检测随机化模式方面的性能.
- 通过特征重要性分析识别分配偏差的预测因素.
主要方法:
- 开发了一个学习方向游戏,用于参与者分配的二分化场景.
- 评估监督的ML模型 (逻辑回归,决策树,支持矢量机) 和无监督的ML模型 (k-means,k-nearest neighbors,人工神经网络).
- 利用合成数据增强来解决样本大小限制和分析特征重要性.
主要成果:
- 监督的ML模型在随机分配的分类中达到87%的最大准确度,特别是在合成数据增强后.
- 无监督模型,包括人工神经网络 (ANN),表现不那么有效,ANN表现出过度匹配.
- 功能重要性分析成功地确定了导致分配偏差的关键预测因素.
结论:
- 机器学习模型,特别是监督方法,显示出验证实验随机化的承诺.
- 基于ML的验证的有效性取决于样本大小和实验设计的复杂性.
- 需要进一步的研究来探索这种方法在各种实验环境中的适用性.
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