A taxonomic look at instance-based stream classifiers

NEUROCOMPUTING

卷:286

Article

Gunn, Iain A. D.¹  Arnaiz-Gonzalez, Alvar²  Kuncheva, Ludmila I.¹ 

[1] Bangor Univ, Sch Comp Sci, Dean St, Bangor LL57 1UT, Gwynedd, Wales

[2] Univ Burgos, Escuela Politecn Super, Burgos 09006, Spain

关键词: Machine learning;    Stream classification;    Instance selection;    Prototype generation;    Concept drift;   

DOI  :  10.1016/j.neucom.2018.01.062

来源: Elsevier

【 摘 要 】

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. (c) 2018 Elsevier B.V. All rights reserved.

【 授权许可】

Free