“分类计数”量化方法的再评价
Re-Assessing the "Classify and Count" Quantification Method
摘要：
学习量化（又称量化）量化）是一项涉及通过监督学习训练类流行率的无偏估计器的任务。这项任务起源于观察，“分类和计数”（CC），获得类流行率估计的平凡方法，往往是一个有偏的估计，因此提供次优的量化精度;根据这一观察，已经提出了几种学习量化的方法，这些方法已经被证明优于CC。在这项工作中，我们认为，以前的作品未能使用适当优化的CC版本。因此，我们重新评估了CC（及其变体）的真正优点，并认为，虽然仍然不如一些尖端的方法，但一旦（a）执行超参数优化，并且（b）通过使用真正的量化损失而不是标准的基于分类的损失来执行这种优化，它们就可以提供接近最先进的准确性。三个公开的二进制情感分类数据集上的实验支持这些结论。
英文摘要：
Learning to quantify (a.k.a.\ quantification) is a task concerned with training unbiased estimators of class prevalence via supervised learning. This task originated with the observation that "Classify and Count" (CC), the trivial method of obtaining class prevalence estimates, is often a biased estimator, and thus delivers suboptimal quantification accuracy; following this observation, several methods for learning to quantify have been proposed that have been shown to outperform CC. In this work we contend that previous works have failed to use properly optimised versions of CC. We thus reassess the real merits of CC (and its variants), and argue that, while still inferior to some cutting-edge methods, they deliver near-state-of-the-art accuracy once (a) hyperparameter optimisation is performed, and (b) this optimisation is performed by using a true quantification loss instead of a standard classification-based loss. Experiments on three publicly available binary sentiment classification datasets support these conclusions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
