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In a broad range of fields it may be desirable to reuse a supervisedclassification algorithm and apply it to a new data set. However,generalization of such an algorithm and thus achieving a similar classificationperformance is only possible when the training data used to build the algorithmis similar to new unseen data one wishes to apply it to. It is often unknown inadvance how an algorithm will perform on new unseen data, being a crucialreason for not deploying an algorithm at all. Therefore, tools are needed tomeasure the similarity of data sets. In this paper, we propose the DataRepresentativeness Criterion (DRC) to determine how representative a trainingdata set is of a new unseen data set. We present a proof of principle, to seewhether the DRC can quantify the similarity of data sets and whether the DRCrelates to the performance of a supervised classification algorithm. Wecompared a number of magnetic resonance imaging (MRI) data sets, ranging fromsubtle to severe difference is acquisition parameters. Results indicate that,based on the similarity of data sets, the DRC is able to give an indication asto when the performance of a supervised classifier decreases. The strictness ofthe DRC can be set by the user, depending on what one considers to be anacceptable underperformance.

The Data Representativeness Criterion: Predicting the Performance of Supervised Classification Based on Data Set Similarity