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

Authors :
Moya, Antonio R.
Veloso, Bruno
Gama, João
Ventura, Sebastián
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]

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