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2021 | OriginalPaper | Chapter

An Under-Sampling Method of Unbalanced Data Classification Based on Support Vector

Authors : Jinqiang He, Yongli Liao, Dengjie Zhu, Xujuan Fan, Xiancong Zhang

Published in: Human Centered Computing

Publisher: Springer International Publishing

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Abstract

To address the problem of unbalanced class distribution of power grid transmission line fault data, the number of fault classes is relatively smaller compared to the number of normal classes, an algorithm based on support vector under-sampling is proposed for transmission line fault classification. The method obtains the support vector on the original data, calculates the distance from the majority of the classes to the \(k\) nearest neighbor support vector, then calculates the class bit statistics to measure the local density information according to the distance, and finally under-samples based on the size of the sample class bit statistics. The bird nest dataset and insulator dataset are selected for performance evaluation, and the results show that this method has a good classification effect on unbalanced data, provides a theoretical reference for unbalanced data classification research, and has a certain practical value in the problem of grid transmission line fault classification.

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Metadata
Title
An Under-Sampling Method of Unbalanced Data Classification Based on Support Vector
Authors
Jinqiang He
Yongli Liao
Dengjie Zhu
Xujuan Fan
Xiancong Zhang
Copyright Year
2021
Book
Human Centered Computing

Print ISBN: 978-3-030-70625-8

Electronic ISBN: 978-3-030-70626-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-70626-5_45

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