Evaluation of machine learning methods for predicting eradication of aquatic invasive species
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In the work, we evaluate the performance
of machine learning approaches for predicting successful
eradication of aquatic invasive species (AIS)
and assess the extent to which eradication of an
invasive species depends on the certain specified
ecological features of the target ecosystem and/or
features that characterize the planned intervention. We
studied the outcomes of 143 planned attempts for
eradicating AIS, where each attempt was described by
ecological and eradication-strategy-related features of
the target ecosystem. We considered several machine
learning approaches to determine whether one could
produce a classifier that accurately predicts weather an
invasive species will be eradicated. To assess each learner’s performance, we examined its tenfold crossvalidated
prediction accuracy as well as the false
positive rate, the F-measure, and the Area Under the
ROC Curve. We also used Kaplan–Meier survival
analysis to determine which features are relevant to
predicting the time required for each eradication
program. Across the five typical machine learning
approaches, our analysis suggests that learners trained
by the decision tree work well, and have the best
performance. In particular, by examining the trained
decision tree model, we found that if an occupied area
was not large and/or containments of AIS dispersal
were employed, the eradication of AIS was likely to be
successful. We also trained decision tree models over
only the ecological features and found that their
performances were comparable with that of models
trained using all features. As our trained decision tree
models are accurate, decision makers can use them to
estimate the result of the proposed actions before they
commit to which specific strategy should be applied
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http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/version/c_b1a7d7d4d402bcce http://purl.org/coar/version/c_71e4c1898caa6e32
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en