A Novel Oversampling Method for Imbalanced Datasets Based on Density Peaks Clustering

Imbalanced data classification is a major challenge in the field of data mining and machine learning, and oversampling algorithms are a widespread technique for re-sampling imbalanced data. To address the problems that existing oversampling methods tend to introduce noise points and generate overlap...

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Autores principales:	Jie Cao*, Yong Shi
Formato:	article
Lenguaje:	EN
Publicado:	Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek 2021
Materias:	classification density peaks clustering imbalanced datasets over sampling Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/a2a5e7d3a4c74626ac5f5226553aa6ff
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id	oai:doaj.org-article:a2a5e7d3a4c74626ac5f5226553aa6ff
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spelling	oai:doaj.org-article:a2a5e7d3a4c74626ac5f5226553aa6ff2021-11-07T00:33:18ZA Novel Oversampling Method for Imbalanced Datasets Based on Density Peaks Clustering1330-36511848-6339https://doaj.org/article/a2a5e7d3a4c74626ac5f5226553aa6ff2021-01-01T00:00:00Zhttps://hrcak.srce.hr/file/383542https://doaj.org/toc/1330-3651https://doaj.org/toc/1848-6339Imbalanced data classification is a major challenge in the field of data mining and machine learning, and oversampling algorithms are a widespread technique for re-sampling imbalanced data. To address the problems that existing oversampling methods tend to introduce noise points and generate overlapping instances, in this paper, we propose a novel oversampling method based on density peaks clustering. Firstly, density peaks clustering algorithm is used to cluster minority instances while screening outlier points. Secondly, sampling weights are assigned according to the size of clustered sub-clusters, and new instances are synthesized by interpolating between cluster cores and other instances of the same sub-cluster. Finally, comparative experiments are conducted on both the artificial data and KEEL datasets. The experiments validate the feasibility and effectiveness of the algorithm and improve the classification accuracy of the imbalanced data.Jie Cao*Yong ShiFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek articleclassificationdensity peaks clusteringimbalanced datasetsover samplingEngineering (General). Civil engineering (General)TA1-2040ENTehnički Vjesnik, Vol 28, Iss 6, Pp 1813-1819 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	classification density peaks clustering imbalanced datasets over sampling Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	classification density peaks clustering imbalanced datasets over sampling Engineering (General). Civil engineering (General) TA1-2040 Jie Cao* Yong Shi A Novel Oversampling Method for Imbalanced Datasets Based on Density Peaks Clustering
description	Imbalanced data classification is a major challenge in the field of data mining and machine learning, and oversampling algorithms are a widespread technique for re-sampling imbalanced data. To address the problems that existing oversampling methods tend to introduce noise points and generate overlapping instances, in this paper, we propose a novel oversampling method based on density peaks clustering. Firstly, density peaks clustering algorithm is used to cluster minority instances while screening outlier points. Secondly, sampling weights are assigned according to the size of clustered sub-clusters, and new instances are synthesized by interpolating between cluster cores and other instances of the same sub-cluster. Finally, comparative experiments are conducted on both the artificial data and KEEL datasets. The experiments validate the feasibility and effectiveness of the algorithm and improve the classification accuracy of the imbalanced data.
format	article
author	Jie Cao* Yong Shi
author_facet	Jie Cao* Yong Shi
author_sort	Jie Cao*
title	A Novel Oversampling Method for Imbalanced Datasets Based on Density Peaks Clustering
title_short	A Novel Oversampling Method for Imbalanced Datasets Based on Density Peaks Clustering
title_full	A Novel Oversampling Method for Imbalanced Datasets Based on Density Peaks Clustering
title_fullStr	A Novel Oversampling Method for Imbalanced Datasets Based on Density Peaks Clustering
title_full_unstemmed	A Novel Oversampling Method for Imbalanced Datasets Based on Density Peaks Clustering
title_sort	novel oversampling method for imbalanced datasets based on density peaks clustering
publisher	Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
publishDate	2021
url	https://doaj.org/article/a2a5e7d3a4c74626ac5f5226553aa6ff
work_keys_str_mv	AT jiecao anoveloversamplingmethodforimbalanceddatasetsbasedondensitypeaksclustering AT yongshi anoveloversamplingmethodforimbalanceddatasetsbasedondensitypeaksclustering AT jiecao noveloversamplingmethodforimbalanceddatasetsbasedondensitypeaksclustering AT yongshi noveloversamplingmethodforimbalanceddatasetsbasedondensitypeaksclustering
_version_	1718443630350303232

A Novel Oversampling Method for Imbalanced Datasets Based on Density Peaks Clustering

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