Exceptional Model Mining meets Multi-objective Optimization

Abstract

Exceptional Model Mining (EMM) is a local pattern mining framework that generalizes subgroup discovery. In EMM, we look for subsets of objects-subgroups-whose model deviates significantly from the same model fitted on the overall dataset. Multi-objective Optimization (MOO) is an area of Multiple Criteria Decision Making where two or more functions need to be optimized at the same time and the goal is to find the best compromise between the concurrent objectives. We introduce a new model class for EMM in a MOO setting called Exceptional Pareto Front Mining. We design fitting quality measures that take into account both the distance between models and the relevance of the subgroups. We propose a beam search for top-K EMM whose added-value is studied on both synthetic and real life datasets. Among others, we discuss a use case on hyperparameter optimization in machine learning for both regression and multi-label classification.