GOOWE: geometrically optimum and online-weighted ensemble classifier for evolving data streams

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buir.contributor.authorCan, Fazlı
dc.citation.epage33en_US
dc.citation.issueNumber2en_US
dc.citation.spage25en_US
dc.citation.volumeNumber12en_US
dc.contributor.authorBonab, H. R.en_US
dc.contributor.authorCan, Fazlıen_US
dc.date.accessioned2019-02-12T08:19:14Z
dc.date.available2019-02-12T08:19:14Z
dc.date.issued2018-01-25en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractDesigning adaptive classifiers for an evolving data stream is a challenging task due to the data size and its dynamically changing nature. Combining individual classifiers in an online setting, the ensemble approach, is a well-known solution. It is possible that a subset of classifiers in the ensemble outperforms others in a time-varying fashion. However, optimum weight assignment for component classifiers is a problem which is not yet fully addressed in online evolving environments. We propose a novel data stream ensemble classifier, called Geometrically Optimum and Online-Weighted Ensemble (GOOWE), which assigns optimum weights to the component classifiers using a sliding window containing the most recent data instances. We map vote scores of individual classifiers and true class labels into a spatial environment. Based on the Euclidean distance between vote scores and ideal-points, and using the linear least squares (LSQ) solution, we present a novel, dynamic, and online weighting approach. While LSQ is used for batch mode ensemble classifiers, it is the first time that we adapt and use it for online environments by providing a spatial modeling of online ensembles. In order to show the robustness of the proposed algorithm, we use real-world datasets and synthetic data generators using the MOA libraries. First, we analyze the impact of our weighting system on prediction accuracy through two scenarios. Second, we compare GOOWE with 8 state-of-the-art ensemble classifiers in a comprehensive experimental environment. Our experiments show that GOOWE provides improved reactions to different types of concept drift compared to our baselines. The statistical tests indicate a significant improvement in accuracy, with conservative time and memory requirements.en_US
dc.identifier.doi10.1145/3139240en_US
dc.identifier.eissn1556-472X
dc.identifier.issn1556-4681
dc.identifier.urihttp://hdl.handle.net/11693/49298
dc.language.isoEnglishen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.isversionofhttps://doi.org/10.1145/3139240en_US
dc.source.titleACM Transactions on Knowledge Discovery from Dataen_US
dc.subjectEnsemble classifieren_US
dc.subjectConcept driften_US
dc.subjectEvolving data streamen_US
dc.subjectDynamic weightingen_US
dc.subjectGeometry of votingen_US
dc.subjectLeast squaresen_US
dc.subjectSpatial modeling for online ensemblesen_US
dc.titleGOOWE: geometrically optimum and online-weighted ensemble classifier for evolving data streamsen_US
dc.typeArticleen_US

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