Identifying Mislabeled Training Data

Understand

This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning.

• The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data.
• Our approach uses a set of learning algorithms to create classifiers that serve as noise filters for the training data.
• We evaluate single algorithm, majority vote and consensus filters on five datasets that are prone to labeling errors.