  As infamous invaders to the North American ecosystem, the Asian giant hornet
(Vespa mandarinia) is devastating not only to native bee colonies, but also to
local apiculture. One of the most effective way to combat the harmful species
is to locate and destroy their nests. By mobilizing the public to actively
report possible sightings of the Asian giant hornet, the governmentcould timely
send inspectors to confirm and possibly destroy the nests. However, such
confirmation requires lab expertise, where manually checking the reports one by
one is extremely consuming of human resources. Further given the limited
knowledge of the public about the Asian giant hornet and the randomness of
report submission, only few of the numerous reports proved positive, i.e.
existing nests. How to classify or prioritize the reports efficiently and
automatically, so as to determine the dispatch of personnel, is of great
significance to the control of the Asian giant hornet. In this paper, we
propose a method to predict the priority of sighting reports based on machine
learning. We model the problem of optimal prioritization of sighting reports as
a problem of classification and prediction. We extracted a variety of rich
features in the report: location, time, image(s), and textual description.
Based on these characteristics, we propose a classification model based on
logistic regression to predict the credibility of a certain report.
Furthermore, our model quantifies the impact between reports to get the
priority ranking of the reports. Extensive experiments on the public dataset
from the WSDA (the Washington State Department of Agriculture) have proved the
effectiveness of our method.
