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Different Strategies of Fitting Logistic Regression for Positive and Unlabelled Data Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Employing One-Class SVM Classifier Ensemble for Imbalanced Data Stream Classification

Authors : Jakub Klikowski, Michał Woźniak

Published in: Computational Science – ICCS 2020

Publisher: Springer International Publishing

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Abstract

The classification of imbalanced data streams is gaining more and more interest. However, apart from the problem that one of the class is not well represented, there are problems typical for data stream classification, such as limited resources, lack of access to the true labels and the possibility of occurrence of the concept drift. Possibility of concept drift appearing enforces design in the method adaptation mechanism. In this article, we propose the OCEIS classifier (One-Class support vector machine classifier Ensemble for Imbalanced data Stream). The main idea is to supply the committee with one-class classifiers trained on clustered data for each class separately. The results obtained from experiments carried out on synthetic and real data show that the proposed method achieves results at a similar level as the state of the art methods compared with it.

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previous chapter Standard Decision Boundary in a Support-Domain of Fuzzy Classifier Prediction for the Task of Imbalanced Data Classification
next chapter Clustering and Weighted Scoring in Geometric Space Support Vector Machine Ensemble for Highly Imbalanced Data Classification
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Metadata
Title
Employing One-Class SVM Classifier Ensemble for Imbalanced Data Stream Classification
Authors
Jakub Klikowski
Michał Woźniak
Copyright Year
2020
Book
Computational Science – ICCS 2020

Print ISBN: 978-3-030-50422-9

Electronic ISBN: 978-3-030-50423-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-50423-6_9

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