{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ensemble-learning-applied-to-classify-gps","title":"Ensemble Learning Applied to Classify GPS Trajectories of Birds into Male or Female","arxiv_id":"1808.08613","date":"2018-08-26","proceeding":null,"authors":["Dewan Fayzur"],"abstract":"We describe our first-place solution to the Animal Behavior Challenge (ABC\n2018) on predicting gender of bird from its GPS trajectory. The task consisted\nin predicting the gender of shearwater based on how they navigate themselves\nacross a big ocean. The trajectories are collected from GPS loggers attached on\nshearwaters' body, and represented as a variable-length sequence of GPS points\n(latitude and longitude), and associated meta-information, such as the sun\nazimuth, the sun elevation, the daytime, the elapsed time on each GPS location\nafter starting the trip, the local time (date is trimmed), and the indicator of\nthe day starting the from the trip. We used ensemble of several variants of\nGradient Boosting Classifier along with Gaussian Process Classifier and Support\nVector Classifier after extensive feature engineering and we ranked first out\nof 74 registered teams. The variants of Gradient Boosting Classifier we tried\nare CatBoost (Developed by Yandex), LightGBM (Developed by Microsoft), XGBoost\n(Developed by Distributed Machine Learning Community). Our approach could\neasily be adapted to other applications in which the goal is to predict a\nclassification output from a variable-length sequence.","url_abs":"http://arxiv.org/abs/1808.08613v1","url_pdf":"http://arxiv.org/pdf/1808.08613v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ensemble-learning-applied-to-classify-gps","repo_url":"https://github.com/dfayzur/Animal-Behavior-Challenge-ABC2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"navigate","task_name":"Navigate"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}