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

IEEE Transactions on Geoscience and Remote Sensing

By:
, , and
DOI: 10.1109/36.911126

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Abstract

Two ensemble methods, bagging and boosting, were investigated for improving algorithm performance. Our results confirmed the theoretical explanation [1] 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.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Enhanced algorithm performance for land cover classification from remotely sensed data using bagging and boosting
Series title:
IEEE Transactions on Geoscience and Remote Sensing
DOI:
10.1109/36.911126
Volume
39
Issue:
3
Year Published:
2001
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
IEEE Transactions on Geoscience and Remote Sensing
First page:
693
Last page:
695