A unifying analysis for the supervised descriptive rule discovery via the weighted relative accuracy

The paper shows how the use of the weighted relative accuracy allows the experts to distinguish between interesting subgroups, emerging and/or contrasting rules thanks to the relation between the quality measures employed in the search process for different models. In addition, this relationship enables us to analyse the main differences and/or similarities between the different techniques within supervised descriptive rule discovery. This scenario opens up new challenges for the supervised descriptive rule learning models in analysing and developing descriptive models with a new perspective.