Identifying Mislabeled Training Data

This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classiication accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classiiers that serve as noise lters for the training data. We evaluate single algorithm, majority vote and consensus lters on ve datasets that are prone to labeling errors. Our experiments illustrate that ltering signiicantly improves classiication accuracy for noise levels up to 30%. An analytical and empirical evaluation of the precision of our approach shows that consensus lters are conservative at throwing away good data at the expense of retaining bad data and that majority lters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus lters are preferable, whereas majority vote lters are preferable for situations with an abundance of data.