Artikel ; Online: A novel framework based on the multi-label classification for dynamic selection of classifiers.
International journal of machine learning and cybernetics
2023 Band 14, Heft 6, Seite(n) 2137–2154
Abstract: Multi-classifier systems (MCSs) are some kind of predictive models that classify instances by combining the output of an ensemble of classifiers given in a pool. With the aim of enhancing the performance of MCSs, dynamic selection (DS) techniques have ... ...
Abstract | Multi-classifier systems (MCSs) are some kind of predictive models that classify instances by combining the output of an ensemble of classifiers given in a pool. With the aim of enhancing the performance of MCSs, dynamic selection (DS) techniques have been introduced and applied to MCSs. Dealing with each test sample classification, DS methods seek to perform the task of classifier selection so that only the most competent classifiers are selected. The principal subject regarding DS techniques is how the competence of classifiers corresponding to every new test sample classification task can be estimated. In traditional dynamic selection methods, for classifying an unknown test sample |
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Sprache | Englisch |
Erscheinungsdatum | 2023-01-02 |
Erscheinungsland | Germany |
Dokumenttyp | Journal Article |
ZDB-ID | 2572473-3 |
ISSN | 1868-808X ; 1868-8071 |
ISSN (online) | 1868-808X |
ISSN | 1868-8071 |
DOI | 10.1007/s13042-022-01751-z |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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