Tree Induction for Probability-Based Ranking
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The time complexity for computing the AUC is with respect to the Hand and Till AUC model and according to Provost and Domingo's AUC model.
“In addition, the time complexity for computing the AUC is with respect to the Hand and Till AUC model [341] and according to Provost and Domingo’s AUC model [336].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [336]
Section 7.1 (page unavailable) describes the paper’s expected-AUC calculation for multiclass data, supporting the reference to a Provost–Domingos AUC approach. It does not give the time complexity of computing AUC, so the complexity part of the attributed claim is unsupported. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAUC is utilized to conduct comparisons between learning algorithms and to construct an optimal learning model.
“It is utilized to conduct comparisons between learning algorithms [336–338], as well as to construct an optimal learning model [339, 340].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [336]
Section 6, “Comparing PETs” (page unavailable), says AUC is used to compare probability-based rankings produced by different classifiers. The paper does not say AUC is used to construct an optimal learning model; its reported AUC results evaluate models rather than describe a model-construction procedure. (LLM, conf=0.84)
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- Authors
- Provost, F., & Domingos, P.
- Affiliations
- New York University (US)
- University of Washington (US)
- Venue
- Machine Learning
- Published
- 2003-09
- Volume
- 52
- Issue
- 3
- Pages
- 199-215
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1509515766
- Cited by
- 541
- References
- 48
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 12 · 2023: 11 · 2022: 15 · 2021: 18 · 2020: 19 · 2019: 16 · 2018: 14 · 2017: 23 · 2016: 15 · 2015: 33 · 2014: 24 · 2013: 38 · 2012: 39
- FWCI
- 81.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Faculty Digital Archive (New York University Florence) (submittedVersion)
- Funders
- National Science Foundation (CAREER)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- class membership probabilities, decision tree induction, Laplace smoothing, bagging, tree size, classification accuracy
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