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A hybrid approach to the problem of class imbalance

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Show simple item record Fitzgerald, Jeannie Ryan, Conor 2015-11-03T14:31:15Z 2015-11-03T14:31:15Z 2013
dc.description peer-reviewed en_US
dc.description.abstract In Machine Learning classification tasks, the class imbalance problem is an important one which has received a lot of attention in the last few years. In binary classification, class imbalance occurs when there are significantly fewer examples of one class than the other. A variety of strategies have been applied to the problem with varying degrees of success. Typically previous approaches have involved attacking the problem either algorithmically or by manipulating the data in order to mitigate the imbalance. We propose a hybrid approach which combines Proportional Individualised Random Sampling(PIRS) with two different fitness functions designed to improve performance on imbalanced classification problems in Genetic Programming. We investigate the efficacy of the proposed methods together with that of five different algorithmic GP solutions, two of which are taken from the recent literature. We conclude that the PIRS approach combined with either average accuracy or Matthews Correlation Coefficient, delivers superior results in terms of AUC score when applied to either balanced or imbalanced datasets. en_US
dc.language.iso eng en_US
dc.relation.ispartofseries International Conference on Soft Computing (MENDEL)
dc.subject genetic programming en_US
dc.subject binary classification en_US
dc.subject class imbalance problem en_US
dc.subject over sampling en_US
dc.subject under sampling en_US
dc.title A hybrid approach to the problem of class imbalance en_US
dc.type info:eu-repo/semantics/conferenceObject en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.contributor.sponsor SFI en_US
dc.relation.projectid 10/IN.1/I3031 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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