Low False Positive Learning With Support Vector Machines

dc.creatorMoraes
dc.creatorD; Wainer
dc.creatorJ; Rocha
dc.creatorA
dc.date2016
dc.date2016-12-06T18:30:59Z
dc.date2016-12-06T18:30:59Z
dc.date.accessioned2018-03-29T02:03:36Z
dc.date.available2018-03-29T02:03:36Z
dc.identifier1095-9076
dc.identifierJournal Of Visual Communication And Image Representation. ACADEMIC PRESS INC ELSEVIER SCIENCE, n. 38, p. 340 - 350.
dc.identifier1047-3203
dc.identifierWOS:000377149100029
dc.identifier10.1016/j.jvcir.2016.03.007
dc.identifierhttp://www-sciencedirect-com.ez88.periodicos.capes.gov.br/science/article/pii/S1047320316300116
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/320183
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1310949
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionMost machine learning systems for binary classification are trained using algorithms that maximize the accuracy and assume that false positives and false negatives are equally bad. However, in many applications, these two types of errors may have very different costs. In this paper, we consider the problem of controlling the false positive rate on SVMs, since its traditional formulation does not'offer such assurance. To solve this problem, we define a feature space sensitive area, where the probability of having false positives is higher, and use a second classifier (unanimity k-NN) in this area to better filter errors and improve the decision-making process. We call this method Risk Area SVM (RA-SVM). We compare the RA-SVM to other state-of-the-art methods for low false positive classification using 33 standard datasets in the literature. The solution we propose shows better performance in the vast majority of the cases using the standard Neyman-Pearson measure. (C) 2016 Elsevier Inc. All rights reserved.
dc.description38
dc.description
dc.description340
dc.description350
dc.descriptionFAPESP [2010/05647-4]
dc.descriptionCNPq [304352/2012-8, 477662/2013-7]
dc.descriptionCAPES/DeepEyes
dc.descriptionMicrosoft Research
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description
dc.description
dc.description
dc.languageEnglish
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCE
dc.publisherSAN DIEGO
dc.relationJournal of Visual Communication and Image Representation
dc.rightsfechado
dc.sourceWOS
dc.subjectSupport Vector Machines
dc.subjectK-nearest Neighbors
dc.subjectLow False Positive Learning
dc.titleLow False Positive Learning With Support Vector Machines
dc.typeArtículos de revistas


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