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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| Published in | IEEE transactions on geoscience and remote sensing Vol. 39; no. 3; pp. 693 - 695 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
01.03.2001
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0196-2892 1558-0644 |
| DOI | 10.1109/36.911126 |
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| Abstract | 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. |
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| AbstractList | 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 Two ensemble methods, bagging and boosting, were investigated for improving algorithm performance. Our results confirmed the theoretical explanation 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. (Author) 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. |
| Author | DeFries, R. Chengquan Huang Jonathan Cheung-Wai Chan |
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| Keywords | algorithms algorithm performance radiometry accuracy remote sensing classification high resolution |
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| References | ref13 ref12 ref14 Bauer (ref3) 1998; 5 ref11 Freund (ref2) Kohonen (ref9) 1995 ref10 Maclin (ref4) ref1 ref7 ref6 Quinlan (ref8) 1993 Breiman (ref5) 1984 |
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| Snippet | Two ensemble methods, bagging and boosting, were investigated for improving algorithm performance. The authors' results confirmed the theoretical explanation... Two ensemble methods, bagging and boosting, were investigated for improving algorithm performance. Our results confirmed the theoretical explanation that... |
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| SubjectTerms | 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 |
| Title | Enhanced algorithm performance for land cover classification from remotely sensed data using bagging and boosting |
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