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Evaluating Intelligent Methods for Decision Making Support in Dermoscopy Based on Information Gain and Ensemble

2021-10-22Book part Open access
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dc.contributor.authorSpolaôr, Newton
dc.contributor.authorFonseca-Pinto, Rui
dc.contributor.authorMendes, Ana I.
dc.contributor.authorEnsina, Leandro A.
dc.contributor.authorTakaki, Weber S. R.
dc.contributor.authorParmezan, Antonio R. S.
dc.contributor.authorNogueira, Conceição V.
dc.contributor.authorCoy, Claudio S. R.
dc.contributor.authorWu, Feng C.
dc.contributor.authorLee, Huei D.
dc.date.accessioned2024-04-01T18:06:03Z
dc.date.available2024-04-01T18:06:03Z
dc.date.issued2021-10-22
dc.description.abstractMelanoma, the most dangerous skin cancer, is sometimes associated with a nevus, a relatively common skin lesion. To find early melanoma, nevus, and other lesions, dermoscopy is often used. In this context, intelligent methods have been applied in dermoscopic images to support decision making. A typical computer-aided diagnosis method comprises three steps: (1) extraction of features that describe image properties, (2) selection of important features previously extracted, (3) classification of images based on the selected features. In this work, traditional data mining approaches underexploited in dermoscopy were applied: information gain for feature selection and an ensemble classification method based on gradient boosting. The former technique ranks image features according to data entropy, while the latter combines the outputs of single classifiers to predict the image class. After evaluating these approaches in a public dataset, we can observe that the results obtained are competitive with the state-of-the-art. Moreover, the presented approach allows a reduction of the total number of features and types of features to produce similar classification scores.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSpolaôr, N. et al. (2021). Evaluating Intelligent Methods for Decision Making Support in Dermoscopy Based on Information Gain and Ensemble. In: Pedrycz, W., Martínez, L., Espin-Andrade, R.A., Rivera, G., Marx Gómez, J. (eds) Computational Intelligence for Business Analytics. Studies in Computational Intelligence, vol 953. 111-127. Springer, Cham. https://doi.org/10.1007/978-3-030-73819-8_7pt_PT
dc.identifier.doihttps://doi.org/10.1007/978-3-030-73819-8_7pt_PT
dc.identifier.eissn978-3-030-73819-8
dc.identifier.issn978-3-030-73818-1
dc.identifier.urihttp://hdl.handle.net/10400.8/9578
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer, Champt_PT
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-030-73819-8_7pt_PT
dc.subjectMachine learningpt_PT
dc.subjectComputer-aided diagnosispt_PT
dc.subjectImage analysispt_PT
dc.titleEvaluating Intelligent Methods for Decision Making Support in Dermoscopy Based on Information Gain and Ensemblept_PT
dc.typebook part
dspace.entity.typePublication
oaire.citation.conferencePlacePart of the Studies in Computational Intelligence book seriespt_PT
oaire.citation.endPage127pt_PT
oaire.citation.startPage111pt_PT
oaire.citation.titleComputational Intelligence for Business Analyticspt_PT
oaire.citation.volume953pt_PT
person.familyNameFonseca-Pinto
person.familyNameMendes
person.familyNameNogueira
person.givenNameRui
person.givenNameAna
person.givenNameConceição
person.identifier.ciencia-id681D-C547-B184
person.identifier.ciencia-id3C19-42EA-DDBD
person.identifier.ciencia-id9C14-CD1D-1298
person.identifier.orcid0000-0001-6774-5363
person.identifier.orcid0000-0002-4161-6130
person.identifier.orcid0000-0002-9269-2221
person.identifier.ridK-9449-2014
person.identifier.scopus-author-id26039086400
person.identifier.scopus-author-id55873199300
person.identifier.scopus-author-id24339135500
rcaap.rightsopenAccesspt_PT
rcaap.typebookPartpt_PT
relation.isAuthorOfPublication7eb9d123-1800-4afd-a2f6-91043353011b
relation.isAuthorOfPublication3523e160-c260-47a6-89cc-eb3fca0059d9
relation.isAuthorOfPublicationb44ae8f7-d99a-4098-ae0b-bbf22db8f2a0
relation.isAuthorOfPublication.latestForDiscoveryb44ae8f7-d99a-4098-ae0b-bbf22db8f2a0

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