Automating Machine Learning Pipeline Design via Metalearning

Abstract

Although Automated Machine Learning (AutoML) systems allow the use of Machine Learning (ML) to automate the design of ML pipelines, they typically search over fixed, task-agnostic configuration spaces, leading to high computational costs. This paper overviews a Ph.D. thesis that proposes a paradigm shift: using Metalearning (MtL) to dynamically build task-specific search spaces. Unlike prior approaches that either optimize within a fixed search space or directly recommend algorithms without an optimization step, this thesis introduces the Dynamic Pipeline CASH problem, which extends the CASH formulation to incorporate meta-model-driven search space creation for pipelines. The thesis contributes a systematic literature review identifying meta-knowledge as the unifying thread across AutoML subfields, applied studies reinforcing the importance of algorithm selection and tuning, a large-scale benchmark of over one million pipeline configurations, and the pymfe package for reproducible meta-feature extraction. These building blocks converge into a novel MtL framework that dynamically reduces search spaces while maintaining competitive performance.