Quantifying counts and costs via classification

By George Forman. 2008

203 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Detecting and Correcting for Label Shift with Black Box Predictors (2018, background): “Post-submission, we learned of antecedents for our estimator in epidemiology (Buck et al., 1966) and revisited by Forman (2008); Saerens et al. (2002).”
Confident Learning: Estimating Uncertainty in Dataset Labels (2019, background): “…in learned model weights from reweighting the loss (Natarajan et al., 2017) with imperfect predicted probabilities, generalizing seminal work Forman (2005, 2008); Lipton et al. (2018) – and (c) Rank which examples to use during training, to allow learning with unnormalized probabilities…”
SemEval-2016 Task 4: Sentiment Analysis in Twitter (2016, background): “Previous work has argued that quantification is not a mere byproduct of classification, since (a) a good classifier is not necessarily a good quantifier, and vice versa, see, e.g., (Forman, 2008); (b) quantification requires evaluation measures different from classification.”
Learning with Complex Loss Functions and Constraints (2018, background): “• Quantification constraints: The problem of quantification aims at estimating the prevalance/frequency of a class in a population [8].”
SemEval-2017 Task 4: Sentiment Analysis in Twitter (2017, background): “We should also note that quantification is not a mere byproduct of classification, as it can be addressed using different approaches and it also needs different evaluation measures (Forman, 2008; Esuli and Sebastiani, 2015).”
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis (2017, methodology): “For the quantiﬁcation tasks an obvious approach is the Classify & Count (Forman, 2008), where we simply compute the fraction of a topic’s messages that a classiﬁer predicts to belong to a class c .”