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Bayesian Network Classifiers
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The Naïve Bayes classifier, which makes strong assumptions about feature independence, is competitive with state-of-the-art classifiers such as C4.5.
“Recent work in supervised learning has demonstrated that a surprisingly simple Bayesian classifier called NB, which makes strong assumptions about feature independence, is competitive with state-of-the-art classifiers such as C4.5.”
From Cost Overrun Risk Assessment and Prediction in Construction Projects: A Bayesian Network Classifier Approach (2022) · cites this paper as [69]
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Metadata
- Authors
- Friedman, N., Geiger, D., & Goldszmidt, M.
- Affiliations
- University of California, Berkeley (US)
- Technion – Israel Institute of Technology (IL)
- Venue
- Machine Learning
- Published
- 1997-11
- Volume
- 29
- Issue
- 2-3
- Pages
- 131-163
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1817561967
- Cited by
- 4,742
- References
- 63
- Citations by year
- 2026: 91 · 2025: 192 · 2024: 138 · 2023: 179 · 2022: 229 · 2021: 272 · 2020: 303 · 2019: 272 · 2018: 244 · 2017: 255 · 2016: 227 · 2015: 228 · 2014: 218 · 2013: 230 · 2012: 195
- FWCI
- 22.24
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Funders
- National Science Foundation (9503109)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Keywords
- Bayesian network classifiers, naive Bayes, tree augmented naive Bayes, feature independence assumption, classifier induction, supervised classification, C4.5 comparison, feature selection
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