Adaptation Strategies for Automated Machine Learning on Evolving Data

TL;DR

This paper investigates how AutoML systems perform under evolving data conditions, specifically concept drift, and proposes adaptation strategies to enhance their robustness across various AutoML approaches.

Contribution

It introduces six concept drift adaptation strategies and evaluates their effectiveness across multiple AutoML methods on real-world and synthetic data streams.

Findings

Adaptation strategies improve AutoML robustness under concept drift.

Certain strategies outperform others depending on the AutoML approach.

Insights lead to recommendations for developing more resilient AutoML systems.

Abstract

Automated Machine Learning (AutoML) systems have been shown to efficiently build good models for new datasets. However, it is often not clear how well they can adapt when the data evolves over time. The main goal of this study is to understand the effect of data stream challenges such as concept drift on the performance of AutoML methods, and which adaptation strategies can be employed to make them more robust. To that end, we propose 6 concept drift adaptation strategies and evaluate their effectiveness on different AutoML approaches. We do this for a variety of AutoML approaches for building machine learning pipelines, including those that leverage Bayesian optimization, genetic programming, and random search with automated stacking. These are evaluated empirically on real-world and synthetic data streams with different types of concept drift. Based on this analysis, we propose ways to develop more sophisticated and robust AutoML techniques.