Land Cover Mapping Using Ensemble Feature Selection Methods
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Date
2008
Authors
Journal Title
Journal ISSN
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Publisher
arXiv preprint arXiv
Abstract
Ensemble classification is an emerging approach to land cover mapping whereby the final classification
output is a result of a ‘consensus’ of classifiers. Intuitively, an ensemble system should consist of base
classifiers which are diverse i.e. classifiers whose decision boundaries err differently. In this paper
ensemble feature selection is used to impose diversity in ensembles. The features of the constituent base
classifiers for each ensemble were created through an exhaustive search algorithm using different
separability indices. For each ensemble, the classification accuracy was derived as well as a diversity
measure purported to give a measure of the in-ensemble diversity. The correlation between ensemble
classification accuracy and diversity measure was determined to establish the interplay between the two
variables. From the findings of this paper, diversity measures as currently formulated do not provide an
adequate means upon which to constitute ensembles for land cover mapping.
Description
Keywords
Ensemble Feature Selection, Diversity, Diversity Measures, Land Cover Mapping
Citation
Gidudu, A., Abe, B., & Marwala, T. (2008). Land Cover Mapping Using Ensemble Feature Selection Methods. arXiv preprint arXiv:0811.2016.