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dc.contributor.authorGeilke, Michaelen_NZ
dc.contributor.authorKarwath, Andreasen_NZ
dc.contributor.authorFrank, Eibeen_NZ
dc.contributor.authorKramer, Stefanen_NZ
dc.date.accessioned2018-08-21T22:27:10Z
dc.date.available2018-05-01en_NZ
dc.date.available2018-08-21T22:27:10Z
dc.date.issued2018en_NZ
dc.identifier.citationGeilke, M., Karwath, A., Frank, E., & Kramer, S. (2018). Online estimation of discrete, continuous, and conditional joint densities using classifier chains. Data Mining and Knowledge Discovery, 32(3), 561–603. https://doi.org/10.1007/s10618-017-0546-6en
dc.identifier.issn1384-5810en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12031
dc.description.abstractWe address the problem of estimating discrete, continuous, and conditional joint densities online, i.e., the algorithm is only provided the current example and its current estimate for its update. The family of proposed online density estimators, estimation of densities online (EDO), uses classifier chains to model dependencies among features, where each classifier in the chain estimates the probability of one particular feature. Because a single chain may not provide a reliable estimate, we also consider ensembles of classifier chains and ensembles of weighted classifier chains. For all density estimators, we provide consistency proofs and propose algorithms to perform certain inference tasks. The empirical evaluation of the estimators is conducted in several experiments and on datasets of up to several millions of instances. In the discrete case, we compare our estimators to density estimates computed by Bayesian structure learners. In the continuous case, we compare them to a state-of-the-art online density estimator. Our experiments demonstrate that, even though designed to work online, EDO delivers estimators of competitive accuracy compared to other density estimators (batch Bayesian structure learners on discrete datasets and the state-of-the-art online density estimator on continuous datasets). Besides achieving similar performance in these cases, EDO is also able to estimate densities with mixed types of variables, i.e., discrete and continuous random variables.
dc.format.mimetypeapplication/pdf
dc.language.isoenen_NZ
dc.publisherSpringeren_NZ
dc.rights© The Author(s) 2017
dc.subjectScience & Technologyen_NZ
dc.subjectTechnologyen_NZ
dc.subjectComputer Science, Artificial Intelligenceen_NZ
dc.subjectComputer Science, Information Systemsen_NZ
dc.subjectComputer Scienceen_NZ
dc.subjectData streamsen_NZ
dc.subjectDensity estimationen_NZ
dc.subjectClassifier chainsen_NZ
dc.subjectInferenceen_NZ
dc.subjectCONSTRAINTSen_NZ
dc.subjectALGORITHMSen_NZ
dc.titleOnline estimation of discrete, continuous, and conditional joint densities using classifier chainsen_NZ
dc.typeJournal Article
dc.identifier.doi10.1007/s10618-017-0546-6en_NZ
dc.relation.isPartOfData Mining and Knowledge Discoveryen_NZ
pubs.begin-page561
pubs.elements-id213030
pubs.end-page603
pubs.issue3en_NZ
pubs.publication-statusPublisheden_NZ
pubs.volume32en_NZ
dc.identifier.eissn1573-756Xen_NZ


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