用于二元量化的连续扫描方法
Continuous Sweep for Binary Quantification Learning
AI总结：
提出Continuous Sweep参数化二元量化器，通过参数化分布和均值替代改进Median Sweep，实现偏差与方差的理论分析及最优决策边界定义，在模拟和实证中表现优异。
摘要：
量化器是一种监督机器学习算法，专注于估计数据集中的类别流行度，而不是为其单个观测值打标签。我们引入了Continuous Sweep，这是一种受表现优异的Median Sweep启发的新型参数化二元量化器，后者是一种基于Adjusted Count估计器的集成方法。我们修改了Median Sweep的两个方面：1）使用参数化类别分布代替真实和假阳性率的经验分布；2）使用一组Adjusted Count估计值的均值代替中位数。这两项修改允许对Continuous Sweep的偏差和方差进行理论分析。此外，偏差和方差的表达式可用于定义集成中要使用的Adjusted Count估计值集合的最优决策边界。我们在三项模拟研究中表明，Continuous Sweep优于Classify、Count和Correct组中的量化器（包括Median Sweep），并且与Distribution Matchers组中两个最佳量化器具有竞争力。此外，还使用这些量化器分析了一个经验数据集，显示出相似的性能。
英文摘要：
A quantifier is a supervised machine learning algorithm, focused on estimating the class prevalence in a dataset rather than labeling its individual observations. We introduce Continuous Sweep, a new parametric binary quantifier inspired by the well-performing Median Sweep, which is an ensemble method based on Adjusted Count estimators. We modified two aspects of Median Sweep: 1) using parametric class distributions instead of empirical distributions for the true and false positive rate; 2) using the mean instead of the median of a set of Adjusted Count estimates. These two modifications allow for a theoretical analysis of the bias and variance of Continuous Sweep. Furthermore, the expressions of bias and variance can be used to define optimal decision boundaries of the set of Adjusted count estimates to be used in the ensemble. We show in three simulation studies that Continuous Sweep outperforms the quantifiers in the group Classify, Count, and Correct, including Median Sweep, and is competitive with the two best quantifiers from the group Distribution Matchers. Also an empirical data set is analysed with these quantifiers showing similar performances.
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