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Klassifikation

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Data Mining

Part of the book series: Computational Intelligence ((CI))

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Zusammenfassung

Klassifikation ist ein überwachtes Lernverfahren , das markierte Daten verwendet, um Objekte zu Klassen zuzuordnen. Es werden falsch positive und falsch negative Fehler unterschieden und auf dieser Basis zahlreiche Klassifikationskriterien definiert. Oft werden Paare solcher Kriterien zur Bewertung von Klassifikatoren verwendet und z. B. in einem ROC- (engl. Receiver Operating Curve) oder PR-Diagramm (engl. Precision Recall) dargestellt. Unterschiedliche Klassifikatoren mit spezifischen Vor- und Nachteilen werden vorgestellt: der naive Bayes-Klassifikator, lineare Diskriminanzanalyse, die Supportvektormaschine auf Basis des Kernel-Tricks, nächste-Nachbarn-Klassifikatoren, lernende Vektorquantifizierung und hierarchische Klassifikation mit Regressionsbäumen.

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Authors and Affiliations

  1. Siemens AG, München, Deutschland

    Thomas A. Runkler Prof. Dr.-Ing.

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  1. Thomas A. Runkler Prof. Dr.-Ing.
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Correspondence to Thomas A. Runkler Prof. Dr.-Ing. .

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Runkler, T. (2015). Klassifikation. In: Data Mining. Computational Intelligence. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-8348-2171-3_8

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