EnsPKDE&IncLKDE: a hybrid time series prediction algorithm integrating dynamic ensemble pruning, incremental learning, and kernel density estimation

Ensemble pruning can effectively overcome several shortcomings of the classical ensemble learning paradigm, such as the relatively high time and space complexity. However, each predictor has its own unique ability. One predictor may not perform well on some samples, but it will perform very well on...

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Vydané v:	Applied intelligence (Dordrecht, Netherlands) Ročník 51; číslo 2; s. 617 - 645
Hlavní autori:	Zhu, Gangliang, Dai, Qun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.02.2021 Springer Nature B.V
Predmet:	Algorithms Artificial Intelligence Computer Science Density distribution Kernels Machine learning Machines Manufacturing Mechanical Engineering Performance prediction Processes Time series Traveling salesman problem One-step-ahead prediction Kernel density estimation (KDE) Incremental learning (IL) Time series prediction (TSP) Dynamic ensemble pruning (DEP)
ISSN:	0924-669X, 1573-7497
On-line prístup:	Získať plný text
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