Can single classifiers be as useful as model ensembles to produce benthic seabed substratum maps?

Can single classifiers be as useful as model ensembles to produce benthic seabed substratum maps? Numerous machine-learning classifiers are available for benthic habitat map production, which can lead to different results. This study highlights the performance of the Random Forest (RF) classifier, which was significantly better than Classification Trees (CT), Naïve Bayes (NB), and a multi-model ensemble in terms of overall accuracy, Balanced Error Rate (BER), Kappa, and area under the curve (AUC) values. RF accuracy was often higher than 90% for each substratum class, even at the most detailed level of the substratum classification and AUC values also indicated excellent performance (0.8–1). Total agreement between classifiers was high at the broadest level of classification (75–80%) when differentiating between hard and soft substratum. However, this sharply declined as the number of substratum categories increased (19–45%) including a mix of rock, gravel, pebbles, and sand. The model ensemble, produced from the results of all three classifiers by majority voting, did not show any increase in predictive performance when compared to the single RF classifier. This study shows how a single classifier may be sufficient to produce benthic seabed maps and model ensembles of multiple classifiers. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Estuarine Coastal and Shelf Science Elsevier

Can single classifiers be as useful as model ensembles to produce benthic seabed substratum maps?

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0272-7714
eISSN
1096-0015
D.O.I.
10.1016/j.ecss.2018.02.028
Publisher site
See Article on Publisher Site

Abstract

Numerous machine-learning classifiers are available for benthic habitat map production, which can lead to different results. This study highlights the performance of the Random Forest (RF) classifier, which was significantly better than Classification Trees (CT), Naïve Bayes (NB), and a multi-model ensemble in terms of overall accuracy, Balanced Error Rate (BER), Kappa, and area under the curve (AUC) values. RF accuracy was often higher than 90% for each substratum class, even at the most detailed level of the substratum classification and AUC values also indicated excellent performance (0.8–1). Total agreement between classifiers was high at the broadest level of classification (75–80%) when differentiating between hard and soft substratum. However, this sharply declined as the number of substratum categories increased (19–45%) including a mix of rock, gravel, pebbles, and sand. The model ensemble, produced from the results of all three classifiers by majority voting, did not show any increase in predictive performance when compared to the single RF classifier. This study shows how a single classifier may be sufficient to produce benthic seabed maps and model ensembles of multiple classifiers.

Journal

Estuarine Coastal and Shelf ScienceElsevier

Published: May 1, 2018

References

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