Evolutionary Stratified Instance Selection applied to Training Set Selection for Extracting High Precise-Interpretable Classification Rules

The generation of predictive models is a frequent task in data mining with the objective of generating high precise and interpretable models. The data reduction is an interesting preprocessing approach that can allow us to obtain predictive models with these characteristics in large size data sets. In this contribution, we analyze the predictive model extraction based on rules using a training selected set via evolutionary stratified instance selection. This method face to the scaling up problem that appears in the evaluation of large size data sets.