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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PFRFCH
Repositorysid.inpe.br/sibgrapi/2017/08.21.22.19
Last Update2017:09.06.17.25.29 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.21.22.19.24
Metadata Last Update2022:06.14.00.08.59 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.23
Citation KeyRodriguesSouzPapa:2017:PrOpFo
TitlePruning Optimum-Path Forest Classifiers Using Multi-Objective Optimization
FormatOn-line
Year2017
Access Date2024, May 01
Number of Files1
Size207 KiB
2. Context
Author1 Rodrigues, Douglas
2 Souza, André Nunes
3 Papa, João Paulo
Affiliation1 Universidade Federal de São Carlos
2 Universidade Estadual de São Paulo
3 Universidade Estadual de São Paulo
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressdouglasrodrigues.dr@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-09-06 01:06:54 :: douglasrodrigues.dr@gmail.com -> administrator :: 2017
2022-06-14 00:08:59 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsOptimum-Path Forest
Meta-heuristic Multi-objective Optimization
Prototype Selection
AbstractMulti-objective optimization plays an important role when one has fitness functions that are somehow conflicting with each other. Also, parameter-dependent machine learning techniques can benefit from such optimization tools. In this paper, we propose a multi-objective-based strategy approach to build compact though representative training sets for Optimum-Path Forest (OPF) learning purposes. Although OPF pruning can provide such a nice representation, it comes with the price of being parameter-dependent. The proposed approach cope with that problem by avoiding the classifier to be hand-tuned by modeling the task of parameter learning as a multi-objective-oriented optimization problem, which can be less prone to errors. Experiments on public datasets show the robustness of the proposed approach, which is now parameterless and user-friendly.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Pruning Optimum-Path Forest...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Pruning Optimum-Path Forest...
doc Directory Contentaccess
source Directory Content
paper.pdf 05/09/2017 22:06 206.7 KiB 
agreement Directory Content
agreement.html 21/08/2017 19:19 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PFRFCH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFRFCH
Languageen
Target Filepaper.pdf
User Groupdouglasrodrigues.dr@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 5
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


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