Classification by voting feature intervals

dc.citation.epage92en_US
dc.citation.spage85en_US
dc.contributor.authorDemiröz, Gülşenen_US
dc.contributor.authorGüvenir, H. Altayen_US
dc.coverage.spatialPrague, Czech Republic
dc.date.accessioned2016-02-08T12:00:05Z
dc.date.available2016-02-08T12:00:05Z
dc.date.issued1997-04en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: 23 - 25 April, 1997
dc.descriptionConference name: ECML '97 Proceedings of the 9th European Conference on Machine Learning
dc.description.abstractA new classification algorithm called VFI (for Voting Feature Intervals) is proposed. A concept is represented by a set of feature intervals on each feature dimension separately. Each feature participates in the classification by distributing real-valued votes among classes. The class receiving the highest vote is declared to be the predicted class. VFI is compared with the Naive Bayesian Classifier, which also considers each feature separately. Experiments on real-world datasets show that VFI achieves comparably and even better than NBC in terms of classification accuracy. Moreover, VFI is faster than NBC on all datasets. © Springer-Verlag Berlin Heidelberg 1997.en_US
dc.identifier.doi10.1007/3-540-62858-4_74
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/27715
dc.language.isoEnglishen_US
dc.publisherSpringeren_US
dc.relation.isversionofhttps://doi.org/10.1007/3-540-62858-4_74
dc.source.titleECML '97 Proceedings of the 9th European Conference on Machine Learningen_US
dc.subjectAlgorithmsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectLearning systemsen_US
dc.subjectClassification accuracyen_US
dc.subjectClassification algorithmen_US
dc.subjectFeature dimensionsen_US
dc.subjectNaive Bayesian Classifieren_US
dc.subjectReal-world datasetsen_US
dc.subjectClassification (of information)en_US
dc.titleClassification by voting feature intervalsen_US
dc.typeConference Paperen_US
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