Classification of high-dimensional evolving data streams via a resource-efficient online ensemble

DOI10.1007/s10618-017-0500-7zbMath1411.62175OpenAlexW2600796512MaRDI QIDQ1741332

Xianqiang Yang

Publication date: 3 May 2019

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-017-0500-7

zbMATH Keywords

high dimensionalityconcept driftdata stream classificationonline ensemble

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Online algorithms; streaming algorithms (68W27)

Related Items (3)

Online active classification via margin-based and feature-based label queries ⋮ Kappa updated ensemble for drifting data stream mining ⋮ A Detailed Study of the Distributed Rough Set Based Locality Sensitive Hashing Feature Selection Technique

Uses Software

• Pegasos
• MOA

Cites Work

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• Pegasos: primal estimated sub-gradient solver for SVM
• Combining block-based and online methods in learning ensembles from concept drifting data streams
• On evaluating stream learning algorithms
• Evaluation methods and decision theory for classification of streaming data with temporal dependence
• Classifying Very-High-Dimensional Data with Random Forests of Oblique Decision Trees
• A survey on concept drift adaptation

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