during data entry  A third cause of labeling error arises when the information used to label each observation is inadequate For example in the medical domain it may not be possible to perform the tests necessary to guarantee that a diagnosis is accurate   For domains in which labeling errors occur  an automated method of eliminating or correcting mislabeled observations will improve the predictive accuracy of the classifier formed from the training d at a In this article we address the problem of identifying training instances that are mis demonstrated that as noise level increases removing noise from labeled  Quinlan attribute information decreases the predictive accuracy of the resulting classifier if the same attribute noise is present in the data to be classified In the case of mislabeled training in stances  class noise  the opposite is true  cleaning the training data will result in a classifier with significantly higher predictive accuracy For example Brodley and Friedl www in the state of the state of the state of the state of the state.

removing mislabeled instances from the training data resulted in higher predictive accuracy relative to classification accu racies achieved without  cleaning" the training data We introduce a method for identifying mislabeled instances that is not specific to any particular learning algorithm  but rather serves as a general method that can be applied to a d at aset before feeding it to a learning algorithm The basic idea is to use a set of learning agorithms to create classifiers that act as a filter for the training data The method is motivated by the technique of removing outliers in regression analysis  Weisberg An outlier is a case an instance that does not follow the same model as the rest of the data and appears as though is comes from a different probability distribution Candidates are cases with a large residual error 1 Weisberg suggests building a model using all of the data except for the suspected outlier and testing whether it does or does not belong to the model using the externally studentized i test Here we apply this idea by using a set of classifiers formed from part of the training data to test whether instances in the remaining part of the training data are mislabeled   An important difference between our work and previous approaches to outlier detection is that our approach assumes that the errors in the class labels are independent of the particular model being fit to the data In essence our method attempts to identify data points that would be outliers in any model We evaluate our approach on five datasets that are prone to labeling errors and we find that filtering substantially improves performance when labels are noisy In addition we compare filtering to majority vote ensemble classifiers to illustrate that although majority vote classifiers provide some protection against noisy data filtering results in significantly higher accuracy A third experiment evaluates the precision of our method in identifying only mislabeled data points We conclude with a discussion of future research directions aimed at minimizing the probability of discarding instances that are exceptions rather than noise