Robust Prototype-Based Learning on Data Streams

In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which allows dynamically modeling time-changing concepts, making predictions in a local fashion. Instead of learning a single model on a fixed or adaptive sliding window of historical data...

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Vydané v:	IEEE transactions on knowledge and data engineering Ročník 30; číslo 5; s. 978 - 991
Hlavní autori:	Shao, Junming, Huang, Feng, Yang, Qinli, Luo, Guangchun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.05.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation models Classification Clustering concept drift Data mining Data models Data stream Data transmission Distributed databases Ensemble learning Evolution Heuristic algorithms Maintenance engineering Modelling Prototypes Statistical analysis Synchronism synchronization
ISSN:	1041-4347, 1558-2191
On-line prístup:	Získať plný text
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