Multi-Objective Automatic Machine Learning with AutoxgboostMC
This addresses the problem for AutoML users needing to balance multiple objectives beyond predictive performance, though it appears incremental as it builds on existing AutoML frameworks.
The paper tackles the limitation of current AutoML systems in optimizing secondary criteria like fairness and interpretability by proposing a method to directly optimize user-defined additional criteria, and demonstrates its usefulness with a simple multi-criteria AutoML system and an application.
AutoML systems are currently rising in popularity, as they can build powerful models without human oversight. They often combine techniques from many different sub-fields of machine learning in order to find a model or set of models that optimize a user-supplied criterion, such as predictive performance. The ultimate goal of such systems is to reduce the amount of time spent on menial tasks, or tasks that can be solved better by algorithms while leaving decisions that require human intelligence to the end-user. In recent years, the importance of other criteria, such as fairness and interpretability, and many others have become more and more apparent. Current AutoML frameworks either do not allow to optimize such secondary criteria or only do so by limiting the system's choice of models and preprocessing steps. We propose to optimize additional criteria defined by the user directly to guide the search towards an optimal machine learning pipeline. In order to demonstrate the need and usefulness of our approach, we provide a simple multi-criteria AutoML system and showcase an exemplary application.