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dc.contributor.authorBifet, Albert
dc.contributor.authorRead, Jesse
dc.contributor.authorŽliobaitė, Indrė
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorHolmes, Geoffrey
dc.coverage.spatialConference held at Prague, Czech Republicen_NZ
dc.identifier.citationBifet, A., Read, J., Žliobaitė, I., Pfahringer, B., & Holmes, G. (2013). Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them. In H. Blockeel et al. (Eds.): ECML PKDD 2013, Part I, LNAI 8188(pp. 465-479). Springer Berlin Heidelberg.en_NZ
dc.description.abstractData stream classification plays an important role in modern data analysis, where data arrives in a stream and needs to be mined in real time. In the data stream setting the underlying distribution from which this data comes may be changing and evolving, and so classifiers that can update themselves during operation are becoming the state-of-the-art. In this paper we show that data streams may have an important temporal component, which currently is not considered in the evaluation and benchmarking of data stream classifiers. We demonstrate how a naive classifier considering the temporal component only outperforms a lot of current state-of-the-art classifiers on real data streams that have temporal dependence, i.e. data is autocorrelated. We propose to evaluate data stream classifiers taking into account temporal dependence, and introduce a new evaluation measure, which provides a more accurate gauge of data stream classifier performance. In response to the temporal dependence issue we propose a generic wrapper for data stream classifiers, which incorporates the temporal component into the attribute space.en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectdata streamsen_NZ
dc.subjecttemporal dependenceen_NZ
dc.subjectMachine learning
dc.titlePitfalls in benchmarking data stream classification and how to avoid themen_NZ
dc.typeConference Contributionen_NZ
dc.relation.isPartOfProc European Conference on Machine Learning and Knowledge Discovery in Databasesen_NZ
pubs.issuePART 1en_NZ
pubs.volumePart I, LNAI 8188en_NZ

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