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Background Check: A General Technique to Build More Reliable and Versatile Classifiers.

, , , and . ICDM, page 1143-1148. IEEE Computer Society, (2016)

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Declaratively Capturing Local Label Correlations with Multi-Label Trees., , and . ECAI, volume 285 of Frontiers in Artificial Intelligence and Applications, page 1467-1475. IOS Press, (2016)Versatile Decision Trees for Learning Over Multiple Contexts., , , and . ECML/PKDD (1), volume 9284 of Lecture Notes in Computer Science, page 184-199. Springer, (2015)Non-parametric Bayesian Isotonic Calibration: Fighting Over-Confidence in Binary Classification., and . ECML/PKDD (2), volume 11907 of Lecture Notes in Computer Science, page 103-120. Springer, (2019)Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration., , , , , and . NeurIPS, page 12295-12305. (2019)Precision-Recall-Gain Curves: PR Analysis Done Right., and . NIPS, page 838-846. (2015)Rate-Oriented Point-Wise Confidence Bounds for ROC Curves., , and . ECML/PKDD (2), volume 8725 of Lecture Notes in Computer Science, page 404-421. Springer, (2014)Reliability Maps: A Tool to Enhance Probability Estimates and Improve Classification Accuracy., and . ECML/PKDD (2), volume 8725 of Lecture Notes in Computer Science, page 18-33. Springer, (2014)Shift Happens: Adjusting Classifiers., , and . ECML/PKDD (2), volume 11907 of Lecture Notes in Computer Science, page 55-70. Springer, (2019)Assuming Locally Equal Calibration Errors for Non-Parametric Multiclass Calibration., and . Trans. Mach. Learn. Res., (2023)Subgroup Discovery with Proper Scoring Rules., , , and . ECML/PKDD (2), volume 9852 of Lecture Notes in Computer Science, page 492-510. Springer, (2016)