On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation

No abstract is available for this paper.

• Publication year

  2013

• Venue

  Neural Information Processing Systems

• Publication date

  2013-12-05

• Fields of study

  Mathematics, Computer Science

• Identifiers

  No identifiers available.

• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Iterative Isotonic Regression

  2013cited by this paper
• Surrogate Regret Bounds for the Area Under the ROC Curve via Strongly Proper Losses

  2013cited by this paper
• Predicting accurate probabilities with a ranking loss

  2012cited by this paper
• Surrogate regret bounds for bipartite ranking via strongly proper losses

  2012cited by this paper
• A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss C` Esar Ferri

  2012cited by this paper
• Calibrated asymmetric surrogate losses

  2012cited by this paper
• Minimax Learning Rates for Bipartite Ranking and Plug-in Rules

  2011cited by this paper
• On Equivalence Relationships Between Classification and Ranking Algorithms

  2011cited by this paper
• Generalized continuous isotonic regression

  2010cited by this paper
• Composite Binary Losses

  2009cited by this paper
• The Isotron Algorithm: High-Dimensional Isotonic Regression

  2009influential reference
• Surrogate regret bounds for proper losses

  2009cited by this paper
• Margin-based Ranking and an Equivalence between AdaBoost and RankBoost

  2009cited by this paper
• Monotone spectral density estimation

  2009cited by this paper
• PAV and the ROC convex hull

  2007influential reference
• Convexity, Classification, and Risk Bounds

  2006cited by this paper
• Cost curves: An improved method for visualizing classifier performance

  2006cited by this paper
• Ranking and empirical minimization of U-statistics

  2006cited by this paper
• Generalization Bounds for the Area Under the ROC Curve

  2005cited by this paper
• Loss Functions for Binary Class Probability Estimation and Classification: Structure and Applications

  2005cited by this paper
• Estimating Class Membership Probabilities using Classifier Learners

  2005cited by this paper
• AUC Optimization vs. Error Rate Minimization

  2003cited by this paper
• Learning When Data Sets are Imbalanced and When Costs are Unequal and Unknown

  2003cited by this paper
• Statistical behavior and consistency of classification methods based on convex risk minimization

  2003cited by this paper
• Loss Functions for Binary Class Probability Estimation

  2003cited by this paper
• Transforming classifier scores into accurate multiclass probability estimates

  2002cited by this paper
• An EÆcient Boosting Algorithm for Combining Preferences

  2001cited by this paper
• An Efficient Boosting Algorithm for Combining Preferences

  1998cited by this paper
• A Probabilistic Theory of Pattern Recognition

  1996cited by this paper
• On the Estimation of Parameters Restricted by Inequalities

  1958cited by this paper
• AN EMPIRICAL DISTRIBUTION FUNCTION FOR SAMPLING WITH INCOMPLETE INFORMATION

  1955cited by this paper

• Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation

  2025cites this paper
• Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification

  2024cites this paper
• Addressing the Binning Problem in Calibration Assessment through Scalar Annotations

  2024cites this paper
• Proper losses regret at least 1/2-order

  2024cites this paper
• On the KL-Divergence-based Robust Satisficing Model

  2024cites this paper
• Fragility Index: A New Approach for Binary Classification

  2023cites this paper
• Does it pay to optimize AUC?

  2023cites this paper
• Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds

  2023influential citation
• Bipartite Ranking Fairness Through a Model Agnostic Ordering Adjustment

  2023cites this paper
• A Symmetric Loss Perspective of Reliable Machine Learning

  2021influential citation
• Meta-Cal: Well-controlled Post-hoc Calibration by Ranking

  2021influential citation
• Automatic role identification for research teams with ranking multi-view machines

  2020cites this paper
• Classification from Ambiguity Comparisons

  2020cites this paper
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility

  2020cites this paper
• Similarity-based Classification: Connecting Similarity Learning to Binary Classification

  2020cites this paper
• Active Classification With Uncertainty Comparison Queries

  2020cites this paper
• xOrder: A Model Agnostic Post-Processing Framework for Achieving Ranking Fairness While Maintaining Algorithm Utility

  2020cites this paper
• Pairwise Supervision Can Provably Elicit a Decision Boundary

  2020cites this paper
• Learning from Outcomes: Evidence-Based Rankings

  2019cites this paper
• On Symmetric Losses for Learning from Corrupted Labels

  2019cites this paper
• The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric

  2019cites this paper
• The Application of Multi-block ADMM on Isotonic Regression Problems

  2019cites this paper
• Surrogate Regret Bounds for Equalized Odds

  2019influential citation
• Classification from Triplet Comparison Data

  2019cites this paper
• L G ] 2 4 Ju l 2 01 9 Classification from Triplet Comparison Data

  2019cites this paper
• Learning Only from Relevant Keywords and Unlabeled Documents

  2019cites this paper
• Risk Bounds for Low Cost Bipartite Ranking

  2019cites this paper
• tau-FPL: Tolerance-Constrained Learning in Linear Time

  2018cites this paper
• Learning a Fast Bipartite Ranker for Text Documents Using Lexicographical Rankers and ROC Curves

  2017cites this paper
• Random Permutation Online Isotonic Regression

  2017cites this paper
• Online Isotonic Regression

  2016cites this paper
• Discovering predictors of mental health service utilization with k-support regularized logistic regression

  2016cites this paper
• Bipartite Ranking: a Risk-Theoretic Perspective

  2016cites this paper
• A ranking-based approach for hierarchical classification

  2015cites this paper
• Fast, Provable Algorithms for Isotonic Regression in all L_p-norms

  2015cites this paper
• Top Rank Optimization in Linear Time

  2014cites this paper
• Bayes-Optimal Scorers for Bipartite Ranking

  2014cites this paper
• Composite Multiclass Losses

  2011cites this paper