Recursive Feature Elimination by Sensitivity Testing.
- DOI
- 10.1109/icmla.2018.00014
- Published
- 2018 Dec
- Container
- Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.1109/icmla.2018.00014,
title = {Recursive Feature Elimination by Sensitivity Testing.},
author = {Escanilla NS and Hellerstein L and Kleiman R and Kuang Z and Shull JD and Page D},
year = {2018},
journal = {Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications},
doi = {10.1109/icmla.2018.00014},
url = {https://doi.org/10.1109/icmla.2018.00014}
}RIS
TY - JOUR TI - Recursive Feature Elimination by Sensitivity Testing. AU - Escanilla NS AU - Hellerstein L AU - Kleiman R AU - Kuang Z AU - Shull JD AU - Page D PY - 2018 JO - Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications DO - 10.1109/icmla.2018.00014 UR - https://doi.org/10.1109/icmla.2018.00014 ER -
APA
NS, E., L, H., R, K., Z, K., JD, S., & D, P. (2018). Recursive Feature Elimination by Sensitivity Testing.. Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications. https://doi.org/10.1109/icmla.2018.00014
Source records
- pubmed · retrieved 2026-09-25T09:01:08.296Z