On Equivalence Relationships Between Classification and Ranking Algorithms

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Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “The AdaBoost algorithm also has the advantage that it maximizes convex proxies for both classification error and area under the ROC curve (AUC) simultaneously [254, 72].”
Predicting accurate probabilities with a ranking loss (2012, methodology): “A similar model for logistic loss was proposed by Ertekin and Rudin (2011).”
Direct Learning to Rank and Rerank (2018, background): “Ertekin and Rudin (2011) provide a discussion and comparison of classiﬁcation versus ranking methods.”