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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2024-01

Autores

Moya, AR (Autor) Outra; Veloso, B (Autor) FEP; João Gama (Autor) FEP; Ventura, S (Autor) Outra

Revista

Título: Data Mining and Knowledge Discovery

Vol. 38

Páginas: 1289-1315

ISSN: 1384-5810

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00Z-M56

DOI: 10.1007/s10618-023-00997-7

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 27