Ensemble Learning Applied to Classify GPS Trajectories of Birds into Male or Female
This work addresses a domain-specific problem in animal behavior classification, but it is incremental as it applies existing ensemble methods to a new dataset.
The paper tackled the problem of predicting the gender of shearwater birds from GPS trajectories by using an ensemble of Gradient Boosting variants, Gaussian Process, and Support Vector Classifiers with feature engineering, achieving first place out of 74 teams in the Animal Behavior Challenge 2018.
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.