Multilayer neural network model for unbalanced data
Classification of unbalanced data often has low performance of the classifier because of the unbalance of data between classes.Using AUC (the area under the ROC curve) as evaluation index,combined with one class F-score feature selection and genetic algorithm,a multilayer neural network model was es...
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2018-06-01
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| Series: | 物联网学报 |
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| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2018.00055/ |
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| author | Xue ZHANG Zhiguo SHI Xuan LIU |
| author_facet | Xue ZHANG Zhiguo SHI Xuan LIU |
| author_sort | Xue ZHANG |
| collection | DOAJ |
| description | Classification of unbalanced data often has low performance of the classifier because of the unbalance of data between classes.Using AUC (the area under the ROC curve) as evaluation index,combined with one class F-score feature selection and genetic algorithm,a multilayer neural network model was established,and a more favorable feature set for unbalanced data classification was selected,so as to establish a deeper model suitable for classification of unbalanced data.Based on Tensor Flow,a multilayer neural network model was established.Using four different UCI datasets for testing,and comparing with the traditional machine learning algorithms such as Naive Bayesian,KNN,neural networks,etc,the performance of the proposed model built on the unbalanced data classification is more excellent. |
| format | Article |
| id | doaj-art-5b7830342381427196f711e3de5e15f9 |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2018-06-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-5b7830342381427196f711e3de5e15f92025-01-15T02:52:03ZzhoChina InfoCom Media Group物联网学报2096-37502018-06-012657259643232Multilayer neural network model for unbalanced dataXue ZHANGZhiguo SHIXuan LIUClassification of unbalanced data often has low performance of the classifier because of the unbalance of data between classes.Using AUC (the area under the ROC curve) as evaluation index,combined with one class F-score feature selection and genetic algorithm,a multilayer neural network model was established,and a more favorable feature set for unbalanced data classification was selected,so as to establish a deeper model suitable for classification of unbalanced data.Based on Tensor Flow,a multilayer neural network model was established.Using four different UCI datasets for testing,and comparing with the traditional machine learning algorithms such as Naive Bayesian,KNN,neural networks,etc,the performance of the proposed model built on the unbalanced data classification is more excellent.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2018.00055/unbalanced dataone class F-score feature selectiongenetic algorithmmultilayer neural network |
| spellingShingle | Xue ZHANG Zhiguo SHI Xuan LIU Multilayer neural network model for unbalanced data 物联网学报 unbalanced data one class F-score feature selection genetic algorithm multilayer neural network |
| title | Multilayer neural network model for unbalanced data |
| title_full | Multilayer neural network model for unbalanced data |
| title_fullStr | Multilayer neural network model for unbalanced data |
| title_full_unstemmed | Multilayer neural network model for unbalanced data |
| title_short | Multilayer neural network model for unbalanced data |
| title_sort | multilayer neural network model for unbalanced data |
| topic | unbalanced data one class F-score feature selection genetic algorithm multilayer neural network |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2018.00055/ |
| work_keys_str_mv | AT xuezhang multilayerneuralnetworkmodelforunbalanceddata AT zhiguoshi multilayerneuralnetworkmodelforunbalanceddata AT xuanliu multilayerneuralnetworkmodelforunbalanceddata |