Re-assessing the “Classify and Count” Quantification Method
نویسندگان
چکیده
Learning to quantify (a.k.a. quantification) is a task concerned with training unbiased estimators of class prevalence via supervised learning. This originated the observation that “Classify and Count” (CC), trivial method obtaining estimates, often biased estimator, thus delivers suboptimal quantification accuracy. Following this observation, several methods for learning have been proposed shown outperform CC. In work we contend previous works failed use properly optimised versions We reassess real merits CC its variants, argue that, while still inferior some cutting-edge methods, they deliver near-state-of-the-art accuracy once (a) hyperparameter optimisation performed, (b) performed by using truly quantification-oriented evaluation protocol. Experiments on three publicly available binary sentiment classification datasets support these conclusions.
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2021
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-030-72240-1_6