Application of Improved MDSMOTE and FC-SVM in Imbalanced Data Set Classification
On the network shopping evaluation data sets appear the phenomenon of extreme imbalance，inorder to improve the classification accuracy of the unbalanced data set，It should be improved from both the sample and the algorithm For one of the problem in MDSMOTE algorithm that when generating part of the...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2018-08-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1561 |
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| Summary: | On the network shopping evaluation data sets appear the phenomenon of extreme imbalance，inorder to improve the classification accuracy of the unbalanced data set，It should be improved from both the sample and the algorithm For one of the problem in MDSMOTE algorithm that when generating part of the new samples， wrong points sample can't be contained，the correct classification of the wrongly classified sample is added to the existing MDSMOTE algorithm to improve the quality of the samples. For that we can' t solve the problem of the hyper plane bias of the minority class in traditional FSVM on imbalanced data sets classification，positive and negative penalty coefficient and fuzzy factor are added the FSVM to improve the recognition rate of unbalanced data. The improved algorithm is used in the classification of JingDong online shopping commentary data set. The f- measure value of this algorithm is increased by 9. 13% on average，which indicates the feasibility and effectiveness of this method. |
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| ISSN: | 1007-2683 |