Stratification for scaling up evolutionary prototype selection

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Local Rule-Based Explanations of Black Box Decision Systems (2018, background): “The usage of classifiers within fitness functions of genetic algorithms can be found in [5, 8, 12, 41].”
Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy (2009, background): “• A study on scalability for making it feasible to apply evolutionary undersampling for very large datasets (Song et al., 2005; Cano et al., 2005).”
A review of instance selection methods (2010, methodology): “Cano et al. (2005) proposed dividing T into r disjoint sets (strata) D1, D2 ,..., Dr of equal size and maintaining the class distribution of T; after the splitting phase, any instance selection method is separately applied over each D j and S is the set of all the instances selected in each D j .I ”
Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability (2007, methodology): “To tackle the scaling problem we combine the stratification of the data sets with the instance selection over them [15].”