Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Detecting Fractures in Classifier Performance
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
A unifying view on dataset shift in classification
Pattern Recognition
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This paper presents Model Monitor (M2), a Java toolkit for robustly evaluating machine learning algorithms in the presence of changing data distributions. M2 provides a simple and intuitive framework in which users can evaluate classifiers under hypothesized shifts in distribution and therefore determine the best model (or models) for their data under a number of potential scenarios. Additionally, M2 is fully integrated with the WEKA machine learning environment, so that a variety of commodity classifiers can be used if desired.