A framework to construct predictive models with logical constraints for table data

Abstract

Reliability of machine learning models are getting serious in the many application areas such as medical and business fields. One approach to addressing these requirements is to use logical constraints representing background knowledge to prevent the model from producing outputs that violate the constraints. However, this approach requires manual setting of all logical constraints for the target task, which is very labor intensive. In this study, we propose a framework that combines RuleFit, a machine learning-based method for automatically acquiring rules, and a method for building predictive models under logical constraints for table data. We evaluate our proposed framework by the prediction accuracy and the violation rate of the constraints using a diabetes benchmark dataset. Using our proposed framework, we achieved to identify the best method from the viewpoint of prediction accuracy and constraint violation rate.