Rescale-invariant SVM for binary classification

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dc.contributor.author Montazery, Mojtaba
dc.contributor.author Wilson, Nic
dc.date.accessioned 2020-12-01T16:49:25Z
dc.date.available 2020-12-01T16:49:25Z
dc.date.issued 2017-08
dc.identifier.citation Montazery, 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.2017/348 en
dc.identifier.startpage 2501 en
dc.identifier.endpage 2507 en
dc.identifier.isbn 978-0-9992411-0-3
dc.identifier.uri http://hdl.handle.net/10468/10800
dc.identifier.doi 10.24963/ijcai.2017/348 en
dc.description.abstract Support 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.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher International Joint Conferences on Artificial Intelligence en
dc.relation.uri https://www.ijcai.org/Proceedings/2017
dc.rights © 2017 International Joint Conferences on Artificial Intelligence en
dc.subject Support Vector Machines (SVM) en
dc.subject Machine learning en
dc.subject AI technology en
dc.subject Artificial intelligence (AI) en
dc.subject Machine learning: classification en
dc.subject Machine learning en
dc.subject Uncertainty in AI: Uncertainty representations en
dc.subject Uncertainty in AI
dc.title Rescale-invariant SVM for binary classification en
dc.type Conference item en
dc.internal.authorcontactother Nic Wilson, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: n.wilson@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2020-11-04T13:06:22Z
dc.description.version Accepted Version en
dc.internal.rssid 542655619
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.internal.copyrightchecked No
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Melbourne, Australia en
dc.internal.IRISemailaddress n.wilson@ucc.ie en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ en


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