包围学习在鸟类GPS轨迹雌雄分类中的应用
Ensemble Learning Applied to Classify GPS Trajectories of Birds into Male or Female
摘要：
我们描述了我们对动物行为挑战赛（ABC 2018）的第一名解决方案，即从GPS轨迹预测鸟类的性别。这项任务包括根据它们如何在大海中航行来预测海鸥的性别。这些轨迹是从附着在海鸥身体上的GPS记录器收集的，并表示为GPS点（纬度和经度）的可变长度序列，以及相关的元信息，例如太阳方位角，太阳仰角，白天，开始旅行后每个GPS位置的经过时间，当地时间（日期被修剪），以及从旅行开始的一天的指示符。经过广泛的特征工程，我们使用了梯度提升分类器的几种变体以及高斯过程分类器和支持向量分类器的集成，我们在74个注册团队中排名第一。我们尝试的梯度提升分类器的变体是CatBoost（由Yandex开发），LightGBM（由Microsoft开发），XGBoost（由分布式机器学习社区开发）。我们的方法可以很容易地适应于其他应用程序，其目标是预测一个分类输出从一个可变长度的序列。
英文摘要：
We describe our first-place solution to the Animal Behavior Challenge (ABC 2018) on predicting gender of bird from its GPS trajectory. The task consisted in predicting the gender of shearwater based on how they navigate themselves across a big ocean. The trajectories are collected from GPS loggers attached on shearwaters' body, and represented as a variable-length sequence of GPS points (latitude and longitude), and associated meta-information, such as the sun azimuth, the sun elevation, the daytime, the elapsed time on each GPS location after starting the trip, the local time (date is trimmed), and the indicator of the day starting the from the trip. We used ensemble of several variants of Gradient Boosting Classifier along with Gaussian Process Classifier and Support Vector Classifier after extensive feature engineering and we ranked first out of 74 registered teams. The variants of Gradient Boosting Classifier we tried are CatBoost (Developed by Yandex), LightGBM (Developed by Microsoft), XGBoost (Developed by Distributed Machine Learning Community). Our approach could easily be adapted to other applications in which the goal is to predict a classification output from a variable-length sequence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
