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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2021-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/383542 |
| _version_ | 1827282212616142848 |
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| author | Jie Cao* Yong Shi |
| author_facet | Jie Cao* Yong Shi |
| author_sort | Jie Cao* |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-24T09:14:20Z |
| format | Article |
| id | doaj.art-ed656071552d4e329cffb8585eee5a99 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:14:20Z |
| publishDate | 2021-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-ed656071552d4e329cffb8585eee5a992024-04-15T17:13:21ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392021-01-012861813181910.17559/TV-20210608123522A Novel Oversampling Method for Imbalanced Datasets Based on Density Peaks ClusteringJie Cao*0Yong Shi1Nanjing University of Information Science & Technology, No. 219, Ningliu Road, Nanjing, Jiangsu, China; Xuzhou University of Technology, No. 2 Lishui Road, Xuzhou, Jiangsu, ChinaNanjing University of Information Science & Technology, School of Mathematics and Statistics, No. 219, Ningliu Road, Nanjing, Jiangsu, ChinaImbalanced 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.https://hrcak.srce.hr/file/383542classificationdensity peaks clusteringimbalanced datasetsover sampling |
| spellingShingle | Jie Cao* Yong Shi A Novel Oversampling Method for Imbalanced Datasets Based on Density Peaks Clustering Tehnički Vjesnik classification density peaks clustering imbalanced datasets over sampling |
| title | 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_short | 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 |
| topic | classification density peaks clustering imbalanced datasets over sampling |
| url | https://hrcak.srce.hr/file/383542 |
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