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2020 | OriginalPaper | Chapter

Clustering and Weighted Scoring in Geometric Space Support Vector Machine Ensemble for Highly Imbalanced Data Classification

Authors : Paweł Ksieniewicz, Robert Burduk

Published in: Computational Science – ICCS 2020

Publisher: Springer International Publishing

Abstract

Learning from imbalanced datasets is a challenging task for standard classification algorithms. In general, there are two main approaches to solve the problem of imbalanced data: algorithm-level and data-level solutions. This paper deals with the second approach. In particular, this paper shows a new proposition for calculating the weighted score function to use in the integration phase of the multiple classification system. The presented research includes experimental evaluation over multiple, open-source, highly imbalanced datasets, presenting the results of comparing the proposed algorithm with three other approaches in the context of six performance measures. Comprehensive experimental results show that the proposed algorithm has better performance measures than the other ensemble methods for highly imbalanced datasets.

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

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Title: Clustering and Weighted Scoring in Geometric Space Support Vector Machine Ensemble for Highly Imbalanced Data Classification
Authors: Paweł Ksieniewicz
Robert Burduk
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_10

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