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“many high-stakes decision making problems involve binary classification, such as financial fraud detection19,20, medical screening25 and credit risk scoring39”
From A benchmarking framework and dataset for learning to defer in human-AI decision-making (2025) · cites this paper as [39]
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Items 201–201 of 201
Relational-View Ensemble Learning for Tabular Prediction
Park, J. E., & Choi, T. J. (2026) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3799682.3839915
Metadata
- Authors
- Hofmann, H.
- Corresponding author
- Hofmann, Hans
- Venue
- UCI Machine Learning Repository
- Publisher
- UCI Machine Learning Repository
- Published
- 1994
- OpenAlex
- W6926258385
- Cited by
- 129
- References
- 0
- Citations by year
- 2026: 28 · 2025: 40 · 2024: 50 · 2023: 10 · 2022: 1
- Type
- dataset
- Language
- en
- Open access
- green
- doi.org
- Locations
- UC Irvine
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- loan classification, bank customers, credit risk
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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