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

2020 | OriginalPaper | Chapter

Standard Decision Boundary in a Support-Domain of Fuzzy Classifier Prediction for the Task of Imbalanced Data Classification

Author : Pawel Ksieniewicz

Published in: Computational Science – ICCS 2020

Publisher: Springer International Publishing

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Abstract

Many real classification problems are characterized by a strong disturbance in a prior probability, which for the most of classification algorithms leads to favoring majority classes. The action most often used to deal with this problem is oversampling of the minority class by the smote algorithm. Following work proposes to employ a modification of an individual binary classifier support-domain decision boundary, similar to the fusion of classifier ensembles done by the Fuzzy Templates method to deal with imbalanced data classification without introducing any repeated or artificial patterns into the training set. The proposed solution has been tested in computer experiments, which results shows its potential in the imbalanced data classification.

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previous chapter A Correction Method of a Base Classifier Applied to Imbalanced Data Classification
next chapter Employing One-Class SVM Classifier Ensemble for Imbalanced Data Stream Classification
Footnotes
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Metadata
Title
Standard Decision Boundary in a Support-Domain of Fuzzy Classifier Prediction for the Task of Imbalanced Data Classification
Author
Pawel Ksieniewicz
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_8

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