Back to Search
Start Over
Improving hyper-parameter self-tuning for data streams by adapting an evolutionary approach.
- Source :
- Data Mining & Knowledge Discovery; May2024, Vol. 38 Issue 3, p1289-1315, 27p
- Publication Year :
- 2024
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- DIFFERENTIAL evolution
ONLINE education
RESEARCH personnel
Subjects
Details
- Language :
- English
- ISSN :
- 13845810
- Volume :
- 38
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Data Mining & Knowledge Discovery
- Publication Type :
- Academic Journal
- Accession number :
- 177044656
- Full Text :
- https://doi.org/10.1007/s10618-023-00997-7