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A quantifier is a supervised machine learning algorithm, focused onestimating the class prevalence in a dataset rather than labeling itsindividual observations. We introduce Continuous Sweep, a new parametric binaryquantifier inspired by the well-performing Median Sweep, which is an ensemblemethod based on Adjusted Count estimators. We modified two aspects of MedianSweep: 1) using parametric class distributions instead of empiricaldistributions for the true and false positive rate; 2) using the mean insteadof the median of a set of Adjusted Count estimates. These two modificationsallow for a theoretical analysis of the bias and variance of Continuous Sweep.Furthermore, the expressions of bias and variance can be used to define optimaldecision boundaries of the set of Adjusted count estimates to be used in theensemble. We show in three simulation studies that Continuous Sweep outperformsthe quantifiers in the group Classify, Count, and Correct, including MedianSweep, and is competitive with the two best quantifiers from the groupDistribution Matchers. Also an empirical data set is analysed with thesequantifiers showing similar performances.

Continuous Sweep for Binary Quantification Learning