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dc.contributor.authorBahri, Marouaen_NZ
dc.contributor.authorGomes, Heitor Muriloen_NZ
dc.contributor.authorBifet, Alberten_NZ
dc.contributor.authorManiu, Silviuen_NZ
dc.coverage.spatialGlasgow, UKen_NZ
dc.date.accessioned2021-04-08T22:14:42Z
dc.date.available2021-04-08T22:14:42Z
dc.date.issued2020en_NZ
dc.identifier.citationBahri, M., Gomes, H. M., Bifet, A., & Maniu, S. (2020). CS-ARF: Compressed adaptive random forests for evolving data stream classification. In Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). Glasgow, UK: IEEE. https://doi.org/10.1109/IJCNN48605.2020.9207188en
dc.identifier.isbn9781728169262en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/14221
dc.description.abstractEnsemble-based methods are one of the most often used methods in the classification task that have been adapted to the stream setting because of their high learning performance achievement. For instance, Adaptive Random Forests (ARF) is a recent ensemble method for evolving data streams that proved to be of a good predictive performance but, as all ensemble methods, it suffers from a severe drawback related to the high computational demand which prevents it from being efficient and further exacerbates with high-dimensional data. In this context, the application of a dimensionality reduction technique is crucial while processing the Internet of Things (IoT) data stream with ultrahigh dimensionality. In this paper, we aim to alleviate this deficiency and improve ARF performance, so we introduce the CS-ARF approach that uses Compressed Sensing (CS) as an internal pre-processing task, to reduce the dimensionality of data before starting the learning process, that will potentially lead to a meaningful improvement in memory usage. Experiments on various datasets show the high classification performance of our CS-ARF approach compared against current state-of-the-art methods while reducing resource usage.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEEen_NZ
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.sourceIJCNN 2020en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectdata stream miningen_NZ
dc.subjectcompressed sensingen_NZ
dc.subjectensemble learningen_NZ
dc.subjectadaptive random forestsen_NZ
dc.subjectMachine learning
dc.titleCS-ARF: Compressed adaptive random forests for evolving data stream classificationen_NZ
dc.typeConference Contribution
dc.identifier.doi10.1109/IJCNN48605.2020.9207188en_NZ
dc.relation.isPartOfProceedings of 2020 International Joint Conference on Neural Networks (IJCNN)en_NZ
pubs.begin-page1
pubs.elements-id258071
pubs.end-page8
pubs.finish-date2020-07-24en_NZ
pubs.publication-statusPublisheden_NZ
pubs.start-date2020-07-19en_NZ


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