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stat.ML2021★ 2 cited
Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class Classification
Chihao Zhang, Yiling Elaine Chen, Shihua Zhang +1
Outcome labeling ambiguity and subjectivity are ubiquitous in real-world datasets. While practitioners commonly combine ambiguous outcome labels for all data points (instances) in…
stat.ML2020★ 2 cited
Bridging Cost-sensitive and Neyman-Pearson Paradigms for Asymmetric Binary Classification
Wei Vivian Li, Xin Tong, Jingyi Jessica Li
Asymmetric binary classification problems, in which the type I and II errors have unequal severity, are ubiquitous in real-world applications. To handle such asymmetry, researchers…