Enhanced algorithm performance for land cover classification from remotely sensed data using bagging and boosting

Two ensemble methods, bagging and boosting, were investigated for improving algorithm performance. The authors' results confirmed the theoretical explanation of L. Breiman (1996) that bagging improves unstable, but not stable, learning algorithms. While boosting enhanced accuracy of a weak lear...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 39; číslo 3; s. 693 - 695
Hlavní autori:	Jonathan Cheung-Wai Chan, Chengquan Huang, DeFries, R.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.03.2001 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Aggregates Applied geophysics Bagging Boosting Earth sciences Earth, ocean, space Exact sciences and technology Geography Image resolution Internal geophysics MODIS Pressing Radiometry Sampling methods Voting algorithms algorithm performance radiometry accuracy remote sensing classification high resolution
ISSN:	0196-2892, 1558-0644
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Popis
Shrnutí:	Two ensemble methods, bagging and boosting, were investigated for improving algorithm performance. The authors' results confirmed the theoretical explanation of L. Breiman (1996) that bagging improves unstable, but not stable, learning algorithms. While boosting enhanced accuracy of a weak learner, its behavior is subject to the characteristics of each learning algorithm.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	0196-2892 1558-0644
DOI:	10.1109/36.911126