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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/43ALKUL
Repositorysid.inpe.br/sibgrapi/2020/09.25.14.27
Last Update2020:09.25.14.27.50 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2020/09.25.14.27.50
Metadata Last Update2022:06.14.00.00.07 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00053
Citation KeyMarcílio-JrEler:2020:AsSHVa
TitleFrom explanations to feature selection: assessing SHAP values as feature selection mechanism
FormatOn-line
Year2020
Access Date2024, Apr. 27
Number of Files1
Size508 KiB
2. Context
Author1 Marcílio-Jr, Wilson Estécio
2 Eler, Danilo Medeiros
Affiliation1 São Paulo State University (UNESP) - Department of Mathematics and Computer Science, Presidente Prudente-SP
2 São Paulo State University (UNESP) - Department of Mathematics and Computer Science, Presidente Prudente-SP
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addresswilson.marcilio@unesp.br
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationPorto de Galinhas (virtual)
Date7-10 Nov. 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2020-09-25 14:27:50 :: wilson.marcilio@unesp.br -> administrator ::
2022-06-14 00:00:07 :: administrator -> wilson.marcilio@unesp.br :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsfeature selection
explainability
AbstractExplainability has become one of the most discussed topics in machine learning research in recent years, and although a lot of methodologies that try to provide explanations to blackbox models have been proposed to address such an issue, little discussion has been made on the pre-processing steps involving the pipeline of development of machine learning solutions, such as feature selection. In this work, we evaluate a game-theoretic approach used to explain the output of any machine learning model, SHAP, as a feature selection mechanism. In the experiments, we show that besides being able to explain the decisions of a model, it achieves better results than three commonly used feature selection algorithms.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2020 > From explanations to...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > From explanations to...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/43ALKUL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/43ALKUL
Languageen
Target FilePID6618233.pdf
User Groupwilson.marcilio@unesp.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2020/10.28.20.46 3
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume
7. Description control
e-Mail (login)wilson.marcilio@unesp.br
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