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dc.contributor.authorNguyen, TT
dc.contributor.authorHa, TS
dc.contributor.authorLuong, AV
dc.contributor.authorLiew, AWC
dc.contributor.authorVan Nguyen, TM
dc.contributor.authorMcCall, J
dc.contributor.editorLopezIbanez, M
dc.date.accessioned2020-01-31T05:57:06Z
dc.date.available2020-01-31T05:57:06Z
dc.date.issued2019
dc.identifier.isbn9781450361118
dc.identifier.doi10.1145/3321707.3321770
dc.identifier.urihttp://hdl.handle.net/10072/391053
dc.description.abstractIn ensemble systems, the predictions of base classifiers are aggregated by a combining algorithm (meta-classifier) to achieve better classification accuracy than using a single classifier. Experiments show that the performance of ensembles significantly depends on the choice of meta-classifier. Normally, the classifier selection method applied to an ensemble usually removes all the predictions of a classifier if this classifier is not selected in the final ensemble. Here we present an idea to only remove a subset of each classifier's prediction thereby introducing a simultaneous meta-data and meta-classifier selection method for ensemble systems. Our approach uses Cross Validation on the training set to generate meta-data as the predictions of base classifiers. We then use Ant Colony Optimization to search for the optimal subset of meta-data and meta-classifier for the data. By considering each column of meta-data, we construct the configuration including a subset of these columns and a meta-classifier. Specifically, the columns are selected according to their corresponding pheromones, and the meta-classifier is chosen at random. The classification accuracy of each configuration is computed based on Cross Validation on meta-data. Experiments on UCI datasets show the advantage of proposed method compared to several classifier and feature selection methods for ensemble systems.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.ispartofconferencenameGenetic and Evolutionary Computation Conference (GECCO '19)
dc.relation.ispartofconferencetitleGECCO '19: Proceedings of the 2019 Genetic and Evolutionary Computation Conference
dc.relation.ispartofdatefrom2019-07-13
dc.relation.ispartofdateto2019-07-17
dc.relation.ispartoflocationPrague, Czech Republic
dc.relation.ispartofpagefrom39
dc.relation.ispartofpagefrom8 pages
dc.relation.ispartofpageto46
dc.relation.ispartofpageto8 pages
dc.subject.fieldofresearchNeural networks
dc.subject.fieldofresearchcode461104
dc.titleSimultaneous meta-data and meta-classifier selection in multiple classifier system
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationNguyen, TT; Ha, TS; Luong, AV; Liew, AWC; Van Nguyen, TM; McCall, J, Simultaneous meta-data and meta-classifier selection in multiple classifier system, GECCO '19: Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 2019, pp. 39-46
dc.date.updated2020-01-31T03:39:22Z
gro.hasfulltextNo Full Text
gro.griffith.authorLiew, Alan Wee-Chung


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