Reference TypeConference Proceedings
Citation KeyFariaSanSarRocTor:2013:WhFeMo
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
TitleClassifier Selection based on the Correlation of Diversity Measures: When Fewer is More
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
Book TitleProceedings
DateAug. 5-8, 2013
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationArequipa, Peru
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.
Tertiary TypeFull Paper
Size875 KiB
Number of Files1
Target Filesibgrapi-2013-camera-ready-paper-114613.pdf
Last Update2013:
Metadata Last Update2020: administrator {D 2013}
Document Stagecompleted
Is the master or a copy?is the master
Content TypeExternal Contribution
source Directory Contentthere are no files
agreement Directory Content
agreement.html 04/07/2013 18:27 0.7 KiB 
History2013-07-04 21:27:32 :: -> administrator ::
2020-02-19 03:09:22 :: administrator -> :: 2013
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
Access Date2020, Nov. 27