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Verbesserung von ML-Algorithmen für unausgewogene Datensätze
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ISBN10: 6208901502
ISBN13: 9786208901509
Publisher: Verlag Unser Wissen
Published: May 27 2025
Pages: 72
Weight: 0.24
Height: 0.17 Width: 6.00 Depth: 9.00
Language: German
ISBN13: 9786208901509
Publisher: Verlag Unser Wissen
Published: May 27 2025
Pages: 72
Weight: 0.24
Height: 0.17 Width: 6.00 Depth: 9.00
Language: German
Die gro e Menge an online generierten Daten hat es Datenwissenschaftlern ermöglicht, diese Informationen zu analysieren und daraus Schlussfolgerungen für verschiedene Bereiche abzuleiten. Echtzeitdaten sind jedoch oft anfällig für Ungleichgewichte, die die Datenqualität beeinträchtigen können und eine gro e Herausforderung im Bereich des maschinellen Lernens darstellen. Auf Stichproben basierende Techniken und auf Algorithmen basierende Modelle sind zwei primäre Methoden, die verwendet werden, um Datenungleichgewichte anzugehen und auszugleichen. In dieser Arbeit werden drei verschiedene Techniken vorgestellt, mit denen sich Ungleichgewichte in Echtzeitdaten auf unterschiedlichen Ebenen bewältigen lassen. Der erste Ansatz schlägt eine auf Stichproben basierende Technik vor, die mit dem Bagging-Mechanismus integriert ist, um Datenungleichgewichte zu behandeln. Das Modell identifiziert ein klassenbasiertes Datenungleichgewicht und führt für jede verfügbare Klasse ein Oversampling durch. Der Bagging-Mechanismus umfasst die Erstellung von Teilmengen der Trainingsdaten und zielt darauf ab, die Ungleichgewichtsniveaus in den Trainingsdaten zu variieren, um eine effektive Vorhersage zu gewährleisten. Trotzdem bleibt der Effekt des Ungleichgewichts im Vorhersagemechanismus bestehen und führt zu einer falschen Klassifizierung mehrerer Minderheitsklassen.
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