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2020 | OriginalPaper | Chapter

A Correction Method of a Base Classifier Applied to Imbalanced Data Classification

Authors : Pawel Trajdos, Marek Kurzynski

Published in: Computational Science – ICCS 2020

Publisher: Springer International Publishing

Abstract

In this paper, the issue of tailoring the soft confusion matrix classifier to deal with imbalanced data is addressed. This is done by changing the definition of the soft neighbourhood of the classified object. The first approach is to change the neighbourhood to be more local by changing the Gaussian potential function approach to the nearest neighbour rule. The second one is to weight the instances that are included in the neighbourhood. The instances are weighted inversely proportional to the a priori class probability. The experimental results show that for one of the investigated base classifiers, the usage of the KNN neighbourhood significantly improves the classification results. What is more, the application of the weighting schema also offers a significant improvement.

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previous chapter On Model Evaluation Under Non-constant Class Imbalance

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

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Title: A Correction Method of a Base Classifier Applied to Imbalanced Data Classification
Authors: Pawel Trajdos
Marek Kurzynski
Publisher: Springer International Publishing
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_7

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