SVM classifier for telecom user arrears based on boundary samples-based under-sampling approaches
Telecom users’ arrears forecasting is a classification problem of unbalanced data set.To deal with the problem that the traditional SVM on the unbalanced date set had a low detection accuracy of minority class,a novel method was proposed.Based on the fact that the position of classification plane wa...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2017-09-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017208/ |
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| _version_ | 1841530207053283328 |
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| author | Chuangchuang LI Guangyue LU Hanglong WANG |
| author_facet | Chuangchuang LI Guangyue LU Hanglong WANG |
| author_sort | Chuangchuang LI |
| collection | DOAJ |
| description | Telecom users’ arrears forecasting is a classification problem of unbalanced data set.To deal with the problem that the traditional SVM on the unbalanced date set had a low detection accuracy of minority class,a novel method was proposed.Based on the fact that the position of classification plane was determined by the boundary samples,the proposed method was implemented via removing some of samples closed to the classification plane to avoid the deficiency of the traditional SVM algorithm.Finally,the proposed method was compared with other approaches on unbalanced data sets.The simulation results show that the proposed method can not only increase the detection accuracy of minority but also improve the overall classification performance. |
| format | Article |
| id | doaj-art-5026de5635d7412f87690636277e448b |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2017-09-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-5026de5635d7412f87690636277e448b2025-01-15T03:06:10ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012017-09-0133859159600164SVM classifier for telecom user arrears based on boundary samples-based under-sampling approachesChuangchuang LIGuangyue LUHanglong WANGTelecom users’ arrears forecasting is a classification problem of unbalanced data set.To deal with the problem that the traditional SVM on the unbalanced date set had a low detection accuracy of minority class,a novel method was proposed.Based on the fact that the position of classification plane was determined by the boundary samples,the proposed method was implemented via removing some of samples closed to the classification plane to avoid the deficiency of the traditional SVM algorithm.Finally,the proposed method was compared with other approaches on unbalanced data sets.The simulation results show that the proposed method can not only increase the detection accuracy of minority but also improve the overall classification performance.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017208/arrearunbalancesupport vector machineboundaryunder-sampling |
| spellingShingle | Chuangchuang LI Guangyue LU Hanglong WANG SVM classifier for telecom user arrears based on boundary samples-based under-sampling approaches Dianxin kexue arrear unbalance support vector machine boundary under-sampling |
| title | SVM classifier for telecom user arrears based on boundary samples-based under-sampling approaches |
| title_full | SVM classifier for telecom user arrears based on boundary samples-based under-sampling approaches |
| title_fullStr | SVM classifier for telecom user arrears based on boundary samples-based under-sampling approaches |
| title_full_unstemmed | SVM classifier for telecom user arrears based on boundary samples-based under-sampling approaches |
| title_short | SVM classifier for telecom user arrears based on boundary samples-based under-sampling approaches |
| title_sort | svm classifier for telecom user arrears based on boundary samples based under sampling approaches |
| topic | arrear unbalance support vector machine boundary under-sampling |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017208/ |
| work_keys_str_mv | AT chuangchuangli svmclassifierfortelecomuserarrearsbasedonboundarysamplesbasedundersamplingapproaches AT guangyuelu svmclassifierfortelecomuserarrearsbasedonboundarysamplesbasedundersamplingapproaches AT hanglongwang svmclassifierfortelecomuserarrearsbasedonboundarysamplesbasedundersamplingapproaches |