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AuthorBellmann, Peterdc.contributor.author
AuthorHihn, Heinkedc.contributor.author
AuthorBraun, Daniel A.dc.contributor.author
AuthorSchwenker, Friedhelmdc.contributor.author
Date of accession2021-01-13T10:30:33Zdc.date.accessioned
Available in OPARU since2021-01-13T10:30:33Zdc.date.available
Date of first publication2020-11-30dc.date.issued
AbstractIn many real-world pattern recognition scenarios, such as in medical applications, the corresponding classification tasks can be of an imbalanced nature. In the current study, we focus on binary, imbalanced classification tasks, i.e.~binary classification tasks in which one of the two classes is under-represented (minority class) in comparison to the other class (majority class). In the literature, many different approaches have been proposed, such as under- or oversampling, to counter class imbalance. In the current work, we introduce a novel method, which addresses the issues of class imbalance. To this end, we first transfer the binary classification task to an equivalent regression task. Subsequently, we generate a set of negative and positive target labels, such that the corresponding regression task becomes balanced, with respect to the redefined target label set. We evaluate our approach on a number of publicly available data sets in combination with Support Vector Machines. Moreover, we compare our proposed method to one of the most popular oversampling techniques (SMOTE). Based on the detailed discussion of the presented outcomes of our experimental evaluation, we provide promising ideas for future research directions.dc.description.abstract
Languageen_USdc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseCC BY 4.0 Internationaldc.rights
Link to license texthttps://creativecommons.org/licenses/by/4.0/dc.rights.uri
KeywordRegressiondc.subject
KeywordImbalanced classification tasksdc.subject
KeywordBinary classificationdc.subject
Dewey Decimal GroupDDC 000 / Computer science, information & general worksdc.subject.ddc
LCSHSupport vector machinesdc.subject.lcsh
LCSHRegression analysisdc.subject.lcsh
TitleBinary classification: counterbalancing class imbalance by applying regression models in combination with one-sided label shiftsdc.title
Resource typePreprintdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-34233dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-34295-2dc.identifier.urn
GNDSupport-Vektor-Maschinedc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Neuroinformatikuulm.affiliationSpecific
Peer reviewneinuulm.peerReview
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category
URL of original publicationhttps://arxiv.org/abs/2011.14764dc.relation1.url
EU project uulmBRISC / Bounded Rationality in Sensorimotor Coordination / EC / H2020 / 678082uulm.projectEU
WoS000661455800078uulm.identifier.wos
Bibliographyuulmuulm.bibliographie


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