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dc.contributor.authorBarddal, Jean Paulen_NZ
dc.contributor.authorEnembreck, Fabrícioen_NZ
dc.contributor.authorGomes, Heitor Muriloen_NZ
dc.contributor.authorBifet, Alberten_NZ
dc.contributor.authorPfahringer, Bernharden_NZ
dc.date.accessioned2019-05-14T03:03:33Z
dc.date.available2019en_NZ
dc.date.available2019-05-14T03:03:33Z
dc.date.issued2019en_NZ
dc.identifier.citationBarddal, J. P., Enembreck, F., Gomes, H. M., Bifet, A., & Pfahringer, B. (2019). Boosting decision stumps for dynamic feature selection on data streams. Information Systems, 83, 13–29. https://doi.org/10.1016/j.is.2019.02.003en
dc.identifier.issn0306-4379en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12538
dc.description.abstractFeature selection targets the identification of which features of a dataset are relevant to the learning task. It is also widely known and used to improve computation times, reduce computation requirements, and to decrease the impact of the curse of dimensionality and enhancing the generalization rates of classifiers. In data streams, classifiers shall benefit from all the items above, but more importantly, from the fact that the relevant subset of features may drift over time. In this paper, we propose a novel dynamic feature selection method for data streams called Adaptive Boosting for Feature Selection (ABFS). ABFS chains decision stumps and drift detectors, and as a result, identifies which features are relevant to the learning task as the stream progresses with reasonable success. In addition to our proposed algorithm, we bring feature selection-specific metrics from batch learning to streaming scenarios. Next, we evaluate ABFS according to these metrics in both synthetic and real-world scenarios. As a result, ABFS improves the classification rates of different types of learners and eventually enhances computational resources usage.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rightsThis is an author’s pre-print of an article published in the journal: Information Systems. © 2019 Elsevier.
dc.subjectcomputer scienceen_NZ
dc.subjectdata stream miningen_NZ
dc.subjectfeature selectionen_NZ
dc.subjectconcept driften_NZ
dc.subjectfeature driften_NZ
dc.subjectMachine learning
dc.titleBoosting decision stumps for dynamic feature selection on data streamsen_NZ
dc.typeJournal Article
dc.identifier.doi10.1016/j.is.2019.02.003en_NZ
dc.relation.isPartOfInformation Systemsen_NZ
pubs.begin-page13
pubs.elements-id235722
pubs.end-page29
pubs.publication-statusPublisheden_NZ
pubs.volume83en_NZ


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