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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3EDGEL2
Repositorysid.inpe.br/sibgrapi/2013/07.04.21.27
Last Update2013:07.04.21.27.32 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2013/07.04.21.27.32
Metadata Last Update2022:06.14.00.07.42 (UTC) administrator
DOI10.1109/SIBGRAPI.2013.12
Citation KeyFariaSanSarRocTor:2013:WhFeMo
TitleClassifier Selection based on the Correlation of Diversity Measures: When Fewer is More
FormatOn-line.
Year2013
Access Date2025, May 11
Number of Files1
Size875 KiB
2. Context
Author1 Faria, Fabio Augusto
2 Santos, Jefersson Alex dos
3 Sarkar, Sudeep
4 Rocha, Anderson
5 Torres, Ricardo da Silva
Affiliation1 University of Campinas
2 University of Campinas
3 University of South Florida
4 University of Campinas
5 University of Campinas
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
e-Mail Addressffaria@ic.unicamp.br
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
Conference LocationArequipa, Peru
Date5-8 Aug. 2013
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2013-07-04 21:27:32 :: ffaria@ic.unicamp.br -> administrator ::
2022-06-14 00:07:42 :: administrator -> :: 2013
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsmultiple classifier system
ensemble of classifiers
diversity measures
coffee crop recognition
AbstractThe ever-growing access to high-resolution images has prompted the development of region-based classification methods for remote sensing images. However, in agricultural applications, the recognition of specific regions is still a challenge as there could be many different spectral patterns in a same studied area. In this context, depending on the features used, different learning methods can be used to create complementary classifiers. Many researchers have developed solutions based on the use of machine learning techniques to address these problems. Examples of successful initiatives are those dedicated to the development of learning techniques for data fusion or Multiple Classifier Systems (MCS). In MCS, diversity becomes an essential factor for their success. Different works have been using diversity measures to select appropriate high-performance classifiers, but the challenge of finding the optimal number of classifiers for a target task has not been properly addressed yet. In general, the proposed solutions rely on the a priori use of ad hoc strategies for selecting classifiers, followed by the evaluation of their effectiveness results during training. Searching by the optimal number of classifiers, however, makes the selection process more expensive. In this paper, we address this issue by proposing a novel strategy for selecting classifiers to be combined based on the correlation of different diversity measures. Diversity measures are used to rank pairs of classifiers and the agreement among ranked lists guides the classifier selection process. A fusion framework has been used in our experiments targeted to the classification of coffee crops in remote sensing images. Experiment results demonstrate that the novel strategy is able to yield comparable effectiveness results when contrasted to several baselines, but using much fewer classifiers.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2013 > Classifier Selection based...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Classifier Selection based...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3EDGEL2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3EDGEL2
Languageen
Target Filesibgrapi-2013-camera-ready-paper-114613.pdf
User Groupffaria@ic.unicamp.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SLB4P
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.04.02 27
sid.inpe.br/sibgrapi/2022/06.10.21.49 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage 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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