Improving hyper-parameter self-tuning for data streams by adapting an evolutionary approach

• Published in: Data mining and knowledge discovery Vol. 38; no. 3; pp. 1289 - 1315
• Main Authors: Moya, Antonio R., Veloso, Bruno, Gama, João, Ventura, Sebastián
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Chemistry and Earth Sciences; Cognitive tasks; Computer Science; Configurations; Data Mining and Knowledge Discovery; Data transmission; Distance learning; Evolutionary algorithms; Evolutionary computation; Genetic algorithms; Information Storage and Retrieval; Machine learning; Mathematical models; Parameters; Physics; S.I. : ECML PKDD 2023; Self tuning; State of the art; Statistics for Engineering; Data streams; Evolutionary algorithms; Concept drift; Optimisation; Hyper-parameters
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-023-00997-7

Hyper-parameter tuning of machine learning models has become a crucial task in achieving optimal results in terms of performance. Several researchers have explored the optimisation task during the last decades to reach a state-of-the-art method. However, most of them focus on batch or offline learning, where data distributions do not change arbitrarily over time. On the other hand, dealing with data streams and online learning is a challenging problem. In fact, the higher the technology goes, the greater the importance of sophisticated techniques to process these data streams. Thus, improving hyper-parameter self-tuning during online learning of these machine learning models is crucial. To this end, in this paper, we present MESSPT, an evolutionary algorithm for self-hyper-parameter tuning for data streams. We apply Differential Evolution to dynamically-sized samples, requiring a single pass-over of data to train and evaluate models and choose the best configurations. We take care of the number of configurations to be evaluated, which necessarily has to be reduced, thus making this evolutionary approach a micro-evolutionary one. Furthermore, we control how our evolutionary algorithm deals with concept drift. Experiments on different learning tasks and over well-known datasets show that our proposed MESSPT outperforms the state-of-the-art on hyper-parameter tuning for data streams.