Ensemble Learning Applied to Classify GPS Trajectories of Birds into Male or Female

TL;DR

This paper presents a winning ensemble machine learning approach that classifies bird gender from GPS trajectories, demonstrating high accuracy through extensive feature engineering and combining multiple classifiers.

Contribution

The study introduces an effective ensemble method using gradient boosting, Gaussian process, and SVM classifiers for variable-length sequence classification of bird GPS data.

Findings

Achieved first place in ABC 2018 challenge

Effective feature engineering for sequence data

Ensemble of gradient boosting, Gaussian process, and SVM classifiers

Abstract

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.