This paper continues to explore noise filtering using learners. Four classification scenarios were investigated. The first scenario applies the more classical approach: training one classifier with a single fit dataset and predicting the test dataset. The second approach is a popular method in data mining: a classifier is built based on the prediction of multiple learners induced on the same dataset. The third approach consists of using the prediction of the same learner induced on multiple fit datasets (multi-dataset classifier). Finally, the most generic approach combines the predictions of multiple learners built on multiple fit datasets and applied on the dataset we want to predict. Such a technique is referred to as multi-learner multi-dataset classifier.

To our knowledge, this empirical work is one of the largest in terms of both scale and scope: 119 (17 9 7) base classification models were built, and more than 700 vectors of base estimates were generated. This paper was published in the Software Quality Journal. You can find more information on Springer and on the ACM portal.

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