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Identifying Bee Species by Means of the Foraging Pattern Using Machine Learning

机译：使用机器学习通过觅食模式识别蜜蜂种类

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摘要

Bees are agents of nature that help provide about one third of the food we eat through a process called pollination. The primary aim of this work is to classify bee species and this was achieved by employing different feature vectors and machine learning algorithms for gathering foraging pattern data, with radio frequency electronic tags glued to the bees' thoraxes. Each time a bee entered or left the hive, the timestamp was stored. The data were analyzed in a time series format, in which the bees' activities were grouped into different categories. The Random Forest algorithm achieved the best results with the area under a ROC curve of 0.94 and 87.41% degree of accuracy, by grouping 12 bees and using 72 attributes.
机译：蜜蜂是自然的媒介，通过称为“授粉”的过程，可以帮助提供大约三分之一的食物。这项工作的主要目的是对蜜蜂种类进行分类，这是通过采用不同的特征向量和机器学习算法来收集觅食模式数据并将射频电子标签粘贴到蜜蜂的胸腔上来实现的。每次蜜蜂进入或离开蜂巢，都会存储时间戳。数据以时间序列格式进行分析，其中蜜蜂的活动分为不同的类别。随机森林算法通过将12只蜜蜂分组并使用72个属性，在ROC曲线下的面积为0.94和准确度为87.41％的情况下获得了最佳结果。

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