Automating model management: a survey on metaheuristics for concept-drift adaptation

Abstract

This study provides an overview of the literature on automated adaptation of machine learning models via metaheuristics, in settings with concept drift. Drift-adaptation of machine learning models presents a high-dimensional optimisation problem; hence, stochastic optimisation via metaheuristics has been a popular choice for finding semi-optimal solutions with low computational costs. Traditionally, automated concept drift adaptation has mainly been studied in the literature on data stream mining; however, as data drift is prevalent in many areas, analogous solutions have been proposed in other fields. Comparing the conceptual solutions across multiple fields is thereby helpful for the overall progress in this area. The found literature is qualitatively classified in terms of relevant aspects of concept drift, adaptation/automation approach and type of metaheuristic. It is found that population-based metaheuristics are by far the most widely used optimisation methods across the domains in the retrieved literature. Methodological problems such as evaluation method and transparency in terms of concept drift type tested in the experiments are discovered and discussed. Over a ten-year period, the usage of metaheuristics in the found literature transitioned from automating single tasks in model development to full model selection in recent years. More transparency in terms of evaluation method and data characteristics is important for future comparison of solutions across drift types and patterns. Furthermore, it is proposed that future studies in this area evaluate the metaheuristics as models themselves, in order to enhance the general understanding of their performance differences in drift adaptation problems.