Rescale-invariant SVM for binary classification

Simple item page
dc.contributor.authorMontazery, Mojtaba
dc.contributor.authorWilson, Nic
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2020-12-01T16:49:25Z
dc.date.available2020-12-01T16:49:25Z
dc.date.issued2017-08
dc.date.updated2020-11-04T13:06:22Z
dc.description.abstractSupport Vector Machines (SVM) are among the best-known machine learning methods, with broad use in different scientific areas. However, one necessary pre-processing phase for SVM is normalization (scaling) of features, since SVM is not invariant to the scales of the features' spaces, i.e., different ways of scaling may lead to different results. We define a more robust decision-making approach for binary classification, in which one sample strongly belongs to a class if it belongs to that class for all possible rescalings of features. We derive a way of characterising the approach for binary SVM that allows determining when an instance strongly belongs to a class and when the classification is invariant to rescaling. The characterisation leads to a computational method to determine whether one sample is strongly positive, strongly negative or neither. Our experimental results back up the intuition that being strongly positive suggests stronger confidence that an instance really is positive.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMontazery, M. and Wilson, N. (2017) 'Rescale-Invariant SVM for Binary Classification', IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia 19-25 August, pp. 2501-2507. doi: 10.24963/ijcai.2017/348en
dc.identifier.doi10.24963/ijcai.2017/348en
dc.identifier.endpage2507en
dc.identifier.isbn978-0-9992411-0-3
dc.identifier.startpage2501en
dc.identifier.urihttps://hdl.handle.net/10468/10800
dc.language.isoenen
dc.publisherInternational Joint Conferences on Artificial Intelligenceen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://www.ijcai.org/Proceedings/2017
dc.rights© 2017 International Joint Conferences on Artificial Intelligenceen
dc.subjectSupport Vector Machines (SVM)en
dc.subjectMachine learningen
dc.subjectAI technologyen
dc.subjectArtificial intelligence (AI)en
dc.subjectMachine learning: classificationen
dc.subjectMachine learningen
dc.subjectUncertainty in AI: Uncertainty representationsen
dc.subjectUncertainty in AI
dc.titleRescale-invariant SVM for binary classificationen
dc.typeConference itemen
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Rescale-Invariant_SVM_for_Binary_Classification-IJCAI17.pdf
Size:
256.9 KB
Format:
Adobe Portable Document Format
Description:
Accepted version
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Computer Science - Conference Items
Insight Centre for Data Analytics - Conference Items