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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/4757

    Título
    A taxonomic look at instance-based stream classifiers
    Autor
    Gunn, Iain A.D. .
    Arnaiz González, ÁlvarAutoridad UBU Orcid
    Kuncheva, Ludmila I. .
    Publicado en
    Neurocomputing. 2012, V. 286, p. 167-178
    Editorial
    Elsevier
    Fecha de publicación
    2018-04
    ISSN
    0925-2312
    DOI
    10.1016/j.neucom.2018.01.062
    Resumen
    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.
    Palabras clave
    Machine learning
    Stream classification
    Instance selection
    Prototype generation
    Concept drift
    Materia
    Computer science
    Informática
    URI
    http://hdl.handle.net/10259/4757
    Versión del editor
    https://doi.org/10.1016/j.neucom.2018.01.062
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    • Artículos ADMIRABLE
    Attribution-NonCommercial-NoDerivatives 4.0 International
    Documento(s) sujeto(s) a una licencia Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
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    Nombre:
    Gunn-Neurocomputing_2018.pdf
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