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Distance Based Concept Drift Detection using Generalized Learning Vector Quantization and Generalized Matrix Learning Vector Quantization

  • Data streams change their statistical behaviour over the time. These changes can occur gradually or abruptly with unforeseen reasons, which may effect the expected outcome. Thus it is important to detect concept drift as soon as it occurs. In this thesis we chose distance based methodology to detect presence of concept drift in the data streams. We used generalized learning vector quantization(GLVQ) and generalized matrix learning vector quantization( GMLVQ) classifiers for distance calculation between prototypes and data points. Chi-square and Kolmogorov–Smirnov tests are used to compare the distance distributions of test and train data sets to indicate the drift presence.

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Author:Ghaziafa Nawaz
Advisor:Thomas Villmann, Marika Kaden
Document Type:Master's Thesis
Year of Completion:2020
Granting Institution:Hochschule Mittweida
Release Date:2023/03/30
GND Keyword:Vektor; Lernendes System; Zeitreise; Maschinelles Lernen
Printexemplar Präsenzbestand
Institutes:Angewandte Computer‐ und Bio­wissen­schaften
DDC classes:006.31 Maschinelles Lernen
Open Access:Innerhalb der Hochschule
Licence (German):License LogoUrheberrechtlich geschützt