Using ensemble margin to explore issues of training data imbalance and mislabeling on large area land cover classification
Andrew Mellor, Samia Boukir, Andrew Haywood, Simon David Jones · 2014
This work introduces new ensemble margin criteria, to evaluate the performance of Random Forests (RF), in the context of large area land cover classification, using imbalanced and noisy training data. Experiments using binary and multiclass classification problems reveal insights into the behaviour of RF over big data, in which training data contains noise and may not be evenly distributed among classes. The margin-based RF performance evaluation is conducted using remote sensing and ancillary spatial data, across a 7.2 million hectare study area.