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Approximation trees: statistical reproducibility in model distillation

Data Mining and Knowledge Discovery(2023)10.1007/s10618-022-00907-3Source: DataRank Database

Approximation trees: statistical reproducibility in model distillation is a research paper published in Data Mining and Knowledge Discovery (2023). On theSindex it has a DataRank of 0. It has been cited 5 times.

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5 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (6 grants)Impact (FWCI 0.65)OA: closedTopics (19)IDs (PubMed, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

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Authors (3)

Zhengze Zhou ORCID,Giles Hooker ORCID,Yichen Zhou ORCID

Live enrichment

PubMed:21097893 OpenAlex:https://openalex.org/W4315647719

National Science Foundation

Grant: NSF DMS-1053252

National Science Foundation

Grant: NSF DEB-1353039

National Institutes of Health

Grant: NIH R03DA036683

National Institutes of Health

Grant: 7R03DA036683-04

Shortening the SOAPP-R: Computer-based opioid risk assessment

National Science Foundation

Grant: 1353039

Collaborative Research: Integral Projection Models for Populations in Varying Environments: Construction and Analysis

National Science Foundation

Grant: 1053252

Diagnostics and Experimental Design in Nonlinear Dynamics

FWCI

0.65

Citation Percentile

0.7%

Citation Trend

2024

2026

closedLicense: Springer Nature TDM

publisherhttps://link.springer.com/content/pdf/10.1007/s10618-022-00907-3.pdf

publisherhttps://link.springer.com/article/10.1007/s10618-022-00907-3/fulltext.html

journalhttps://doi.org/10.1007/s10618-022-00907-3

Fields of Study

Explainable Artificial Intelligence (XAI)Machine Learning and Data ClassificationAdversarial Robustness in Machine Learning0202 electrical engineering, electronic engineering, information engineering02 engineering and technology0101 mathematics01 natural sciences

Keywords

DistillationRandomnessComputer scienceReproducibilityTree (set theory)Process (computing)Machine learningMathematicsArtificial intelligenceStatisticsChemistryChromatography

Approximation trees: statistical reproducibility in model distillation

FAIR Checklist

Authors (3)

Funding (6 grants)

Impact

Open Access

Topics