2026-04-17T11:32:19Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/47572022-04-29T12:02:45Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220
Repositorio Institucional de la Universidad de Burgos
author
Gunn, Iain A.D. .
author
Arnaiz González, Álvar
author
Kuncheva, Ludmila I. .
2018-03-20T10:57:20Z
2018-04
0925-2312
http://hdl.handle.net/10259/4757
10.1016/j.neucom.2018.01.062
Large numbers of data streams are today generated in many fields. A key challenge when learning from such streams is the problem of concept drift. Many methods, including many prototype methods, have been proposed in recent years to address this problem. This paper presents a refined taxonomy of instance selection and generation methods for the classification of data streams subject to concept drift. The taxonomy allows discrimination among a large number of methods which pre-existing taxonomies for offline instance selection methods did not distinguish. This makes possible a valuable new perspective on experimental results, and provides a framework for discussion of the concepts behind different algorithm-design approaches. We review a selection of modern algorithms for the purpose of illustrating the distinctions made by the taxonomy. We present the results of a numerical experiment which examined the performance of a number of representative methods on both synthetic and real-world data sets with and without concept drift, and discuss the implications for the directions of future research in light of the taxonomy. On the basis of the experimental results, we are able to give recommendations for the experimental evaluation of algorithms which may be proposed in the future.
eng
Attribution-NonCommercial-NoDerivatives 4.0 International
Machine learning
Stream classification
Instance selection
Prototype generation
Concept drift
A taxonomic look at instance-based stream classifiers
info:eu-repo/semantics/article
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URL
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