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An oversampling method for multi-class imbalanced data based on composite weights
To solve the oversampling problem of multi-class small samples and to improve their classification accuracy, we develop an oversampling method based on classification ranking and weight setting. The designed oversampling algorithm sorts the data within each class of dataset according to the distance...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589211/ https://www.ncbi.nlm.nih.gov/pubmed/34767567 http://dx.doi.org/10.1371/journal.pone.0259227 |
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author | Deng, Mingyang Guo, Yingshi Wang, Chang Wu, Fuwei |
author_facet | Deng, Mingyang Guo, Yingshi Wang, Chang Wu, Fuwei |
author_sort | Deng, Mingyang |
collection | PubMed |
description | To solve the oversampling problem of multi-class small samples and to improve their classification accuracy, we develop an oversampling method based on classification ranking and weight setting. The designed oversampling algorithm sorts the data within each class of dataset according to the distance from original data to the hyperplane. Furthermore, iterative sampling is performed within the class and inter-class sampling is adopted at the boundaries of adjacent classes according to the sampling weight composed of data density and data sorting. Finally, information assignment is performed on all newly generated sampling data. The training and testing experiments of the algorithm are conducted by using the UCI imbalanced datasets, and the established composite metrics are used to evaluate the performance of the proposed algorithm and other algorithms in comprehensive evaluation method. The results show that the proposed algorithm makes the multi-class imbalanced data balanced in terms of quantity, and the newly generated data maintain the distribution characteristics and information properties of the original samples. Moreover, compared with other algorithms such as SMOTE and SVMOM, the proposed algorithm has reached a higher classification accuracy of about 90%. It is concluded that this algorithm has high practicability and general characteristics for imbalanced multi-class samples. |
format | Online Article Text |
id | pubmed-8589211 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-85892112021-11-13 An oversampling method for multi-class imbalanced data based on composite weights Deng, Mingyang Guo, Yingshi Wang, Chang Wu, Fuwei PLoS One Research Article To solve the oversampling problem of multi-class small samples and to improve their classification accuracy, we develop an oversampling method based on classification ranking and weight setting. The designed oversampling algorithm sorts the data within each class of dataset according to the distance from original data to the hyperplane. Furthermore, iterative sampling is performed within the class and inter-class sampling is adopted at the boundaries of adjacent classes according to the sampling weight composed of data density and data sorting. Finally, information assignment is performed on all newly generated sampling data. The training and testing experiments of the algorithm are conducted by using the UCI imbalanced datasets, and the established composite metrics are used to evaluate the performance of the proposed algorithm and other algorithms in comprehensive evaluation method. The results show that the proposed algorithm makes the multi-class imbalanced data balanced in terms of quantity, and the newly generated data maintain the distribution characteristics and information properties of the original samples. Moreover, compared with other algorithms such as SMOTE and SVMOM, the proposed algorithm has reached a higher classification accuracy of about 90%. It is concluded that this algorithm has high practicability and general characteristics for imbalanced multi-class samples. Public Library of Science 2021-11-12 /pmc/articles/PMC8589211/ /pubmed/34767567 http://dx.doi.org/10.1371/journal.pone.0259227 Text en © 2021 Deng et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Deng, Mingyang Guo, Yingshi Wang, Chang Wu, Fuwei An oversampling method for multi-class imbalanced data based on composite weights |
title | An oversampling method for multi-class imbalanced data based on composite weights |
title_full | An oversampling method for multi-class imbalanced data based on composite weights |
title_fullStr | An oversampling method for multi-class imbalanced data based on composite weights |
title_full_unstemmed | An oversampling method for multi-class imbalanced data based on composite weights |
title_short | An oversampling method for multi-class imbalanced data based on composite weights |
title_sort | oversampling method for multi-class imbalanced data based on composite weights |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589211/ https://www.ncbi.nlm.nih.gov/pubmed/34767567 http://dx.doi.org/10.1371/journal.pone.0259227 |
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