Identifying and Correcting Mislabeled Satellite Image Data by Iterative Ordering of Ensemble Margins
Samia Boukir, Wenjie Feng · 2019
The accuracy of a supervised classifier is directly influenced by the quality of the training data used. However, real-world data often suffers from mislabelling issues. To handle the mislabeling problem, we propose an ensemble margin-based mislabeled training data identification, elimination and correction approach based on data ordering. A powerful ensemble method, random forest, is at the core of our algorithms design. The effectiveness of our methods is demonstrated in performing mapping of land covers. A comparative analysis is conducted with respect to the majority vote filter, a popular ensemble-based mislabeled data filter.