Identifying and Eliminating Mislabeled Training Instances

Carla E. Brodley, Mark A. Friedl

This paper presents a new approach to identifying and eliminating mislabeled training instances. The goal of this technique is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. The approach employs an ensemble of classifiers that serve as a filter for the training data. Using an n-fold cross validation, the training data is passed through the filter. Only instances that the filter classifies correctly are passed to the final learning algorithm. We present an empirical evaluation of the approach for the task of automated land cover mapping from remotely sensed data. Labeling error arises in these data from a multitude of sources including lack of consistency in the vegetation classification used, variable measurement techniques, and variation in the spatial sampling resolution. Our evaluation shows that for noise levels of less than 40%, filtering results in higher predictive accuracy than not filtering, and for levels of class noise less than or equal to 20% filtering allows the base-line accuracy to be retained. Our empirical results suggest that the ensemble filter approach is an effective method for identifying labeling errors, and further, that the approach will significantly benefit ongoing research to develop accurate and robust remote sensing-based methods to map land cover at global scales.

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