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Robust Classification for Imprecise Environments
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Expected cost searches for the decision threshold that minimises cost given the misclassification costs.
“In contrast to net benefit, expected cost searches for the decision threshold that minimises cost given the misclassification costs.”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [88]
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Metadata
- Authors
- Provost, F., & Fawcett, T.
- Affiliations
- New York University (US)
- Hewlett-Packard (United States) (US)
- Venue
- Machine Learning
- Published
- 2001-03
- Volume
- 42
- Issue
- 3
- Pages
- 203-231
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2096942889
- Cited by
- 1,250
- References
- 45
- Citations by year
- 2026: 35 · 2025: 29 · 2024: 23 · 2023: 27 · 2022: 42 · 2021: 41 · 2020: 57 · 2019: 40 · 2018: 42 · 2017: 37 · 2016: 44 · 2015: 40 · 2014: 61 · 2013: 55 · 2012: 50
- FWCI
- 51.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Faculty Digital Archive (New York University Florence) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- robust classification, hybrid classifier, misclassification cost, classifier comparison, decision analysis, computational geometry, sensitivity analysis, expected cost
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