Pavement Crack Rating Using Machine Learning Frameworks: Partitioning, Bootstrap Forest, Boosted Trees, Naïve Bayes, and <i>K</i> -Nearest Neighbors

Research article (Journal of Transportation Engineering Part B Pavements, 2019) · cited 52× · AI/ML

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Pavement Crack Rating Using Machine Learning Frameworks: Partitioning, Bootstrap Forest, Boosted Trees, Naïve Bayes, and K -Nearest Neighbors

Summary

Pavement Crack Rating Using Machine Learning Frameworks: Partitioning, Bootstrap Forest, Boosted Trees, Naïve Bayes, and K -Nearest Neighbors is a scholarly article^[1].

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Pavement Crack Rating Using Machine Learning Frameworks: Partitioning, Bootstrap Forest, Boosted Trees, Naïve Bayes, and K -Nearest Neighbors's instance of is recorded as scholarly article^[2].

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Authored by 3

Adrian Barbu, John Sobanjo, Sylvester Inkoom

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Adrian Barbu, John Sobanjo, Sylvester Inkoom

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[2] ↑ Pavement Crack Rating Using Machine Learning Frameworks: Partitioning, Bootstrap Forest, Boosted Trees, Naïve Bayes, and K -Nearest Neighbors — instance of (P31): scholarly article. wikidata.org.

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[1] ↑ Pavement Crack Rating Using Machine Learning Frameworks: Partitioning, Bootstrap Forest, Boosted Trees, Naïve Bayes, and K -Nearest Neighbors is an instance of scholarly article (scholarly article) [P31, primary]. Wikidata. wikidata.org.

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4ort.xyz Knowledge Graph. (2026). Pavement Crack Rating Using Machine Learning Frameworks: Partitioning, Bootstrap Forest, Boosted Trees, Naïve Bayes, and <i>K</i> -Nearest Neighbors. Retrieved May 24, 2026, from https://4ort.xyz/entity/pavement-crack-rating-using-machine-learning-frameworks-partitioning-bootstrap-forest-boosted-trees-naive-bayes-and-i-k-

MLA

“Pavement Crack Rating Using Machine Learning Frameworks: Partitioning, Bootstrap Forest, Boosted Trees, Naïve Bayes, and <i>K</i> -Nearest Neighbors.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/pavement-crack-rating-using-machine-learning-frameworks-partitioning-bootstrap-forest-boosted-trees-naive-bayes-and-i-k-.

BibTeX

@misc{4ortxyz_pavement-crack-rating-using-machine-learning-frameworks-partitioning-bootstrap-forest-boosted-trees-naive-bayes-and-i-k-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Pavement Crack Rating Using Machine Learning Frameworks: Partitioning, Bootstrap Forest, Boosted Trees, Naïve Bayes, and <i>K</i> -Nearest Neighbors}}, year = {2026}, url = {https://4ort.xyz/entity/pavement-crack-rating-using-machine-learning-frameworks-partitioning-bootstrap-forest-boosted-trees-naive-bayes-and-i-k-}, note = {Accessed: 2026-05-24}}

LLM prompt

According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Pavement Crack Rating Using Machine Learning Frameworks: Partitioning, Bootstrap Forest, Boosted Trees, Naïve Bayes, and <i>K</i> -Nearest Neighbors — https://4ort.xyz/entity/pavement-crack-rating-using-machine-learning-frameworks-partitioning-bootstrap-forest-boosted-trees-naive-bayes-and-i-k- (retrieved 2026-05-24)

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