Boosted Noise Filters for Identifying Mislabeled Data

In many practical classification problems, mislabeled data instances (i.e., class noise) exist in the acquired (training) data and often have a detrimental effect on the classification performance. Identifying such noisy instances and removing them from training data can significantly improve the trained classifiers. One such effective noise detector is the so-called ensemble filter, which predicts the instances misclassified by multiple learned classifiers as noise. This paper proposes a novel noise detection method that uses a boosting ensemble of the ensemble noise filters. Multiple ensemble noise filters are built sequentially, with each one working on weighted instances. The weighting scheme follows the general boosting idea and reduces the weights of those instances that are confidently predicted as noise in previous runs. This method essentially wraps an existing ensemble filter-based noise detector with a second layer of boosting ensemble. Our experimental results on a range of real datasets from the UCI repository show the superiority of the proposed boosted noise detectors.