Which standard classification algorithm has more stable performance for imbalanced network traffic data?
Abstract Most standard classification algorithms are difficult to effectively learn and predict from imbalanced network traffic data, which usually leads to lower classification accuracy. To analyze the influence of imbalanced network traffic data on the performance of standard classification algori...
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| Autor*in: |
Zheng, Ming [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
Imbalanced network traffic data |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 28(2023), 1 vom: 26. Okt., Seite 217-234 |
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| Übergeordnetes Werk: |
volume:28 ; year:2023 ; number:1 ; day:26 ; month:10 ; pages:217-234 |
| Links: |
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| DOI / URN: |
10.1007/s00500-023-09331-1 |
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SPR054258472 |
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| 520 | |a Abstract Most standard classification algorithms are difficult to effectively learn and predict from imbalanced network traffic data, which usually leads to lower classification accuracy. To analyze the influence of imbalanced network traffic data on the performance of standard classification algorithms, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced network traffic data set with gradually varying Imbalance Ratio (IR) and belonging to the same distribution. Then, to obtain more objective classification result and simplify the evaluation process, the evaluation metric AFG is used to evaluate the classification performance of standard classification algorithms based on area under the receiver operating characteristic curve (AUC), F-measure and G-mean. Finally, based on AFG and coefficient of variation (CV), performance stability of standard classification algorithms on imbalanced network traffic data is obtained. Experiments of eight widely used standard classification algorithms on 25 different imbalanced network traffic data demonstrate that the classification performance of GNB, RF and DT is unstable, while BNB, KNN, LR, GBDT, and SVC are relatively stable and not susceptible to imbalanced data. Especially, the KNN has the most stable classification performance. Also, the results are statistically confirmed by Friedman and Nemenyi post hoc statistical tests. | ||
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| 700 | 1 | |a Guo, Liangmin |4 aut | |
| 700 | 1 | |a Chen, Fulong |4 aut | |
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10.1007/s00500-023-09331-1 doi (DE-627)SPR054258472 (SPR)s00500-023-09331-1-e DE-627 ger DE-627 rakwb eng Zheng, Ming verfasserin (orcid)0000-0001-9001-0859 aut Which standard classification algorithm has more stable performance for imbalanced network traffic data? 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Most standard classification algorithms are difficult to effectively learn and predict from imbalanced network traffic data, which usually leads to lower classification accuracy. To analyze the influence of imbalanced network traffic data on the performance of standard classification algorithms, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced network traffic data set with gradually varying Imbalance Ratio (IR) and belonging to the same distribution. Then, to obtain more objective classification result and simplify the evaluation process, the evaluation metric AFG is used to evaluate the classification performance of standard classification algorithms based on area under the receiver operating characteristic curve (AUC), F-measure and G-mean. Finally, based on AFG and coefficient of variation (CV), performance stability of standard classification algorithms on imbalanced network traffic data is obtained. Experiments of eight widely used standard classification algorithms on 25 different imbalanced network traffic data demonstrate that the classification performance of GNB, RF and DT is unstable, while BNB, KNN, LR, GBDT, and SVC are relatively stable and not susceptible to imbalanced data. Especially, the KNN has the most stable classification performance. Also, the results are statistically confirmed by Friedman and Nemenyi post hoc statistical tests. Imbalanced network traffic data (dpeaa)DE-He213 Data augmentation algorithms (dpeaa)DE-He213 Standard classification algorithms (dpeaa)DE-He213 Stable classification performance (dpeaa)DE-He213 Ma, Kai aut Wang, Fei aut Hu, Xiaowen aut Yu, Qingying aut Guo, Liangmin aut Chen, Fulong aut Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 1 vom: 26. Okt., Seite 217-234 (DE-627)SPR006469531 nnns volume:28 year:2023 number:1 day:26 month:10 pages:217-234 https://dx.doi.org/10.1007/s00500-023-09331-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 1 26 10 217-234 |
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10.1007/s00500-023-09331-1 doi (DE-627)SPR054258472 (SPR)s00500-023-09331-1-e DE-627 ger DE-627 rakwb eng Zheng, Ming verfasserin (orcid)0000-0001-9001-0859 aut Which standard classification algorithm has more stable performance for imbalanced network traffic data? 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Most standard classification algorithms are difficult to effectively learn and predict from imbalanced network traffic data, which usually leads to lower classification accuracy. To analyze the influence of imbalanced network traffic data on the performance of standard classification algorithms, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced network traffic data set with gradually varying Imbalance Ratio (IR) and belonging to the same distribution. Then, to obtain more objective classification result and simplify the evaluation process, the evaluation metric AFG is used to evaluate the classification performance of standard classification algorithms based on area under the receiver operating characteristic curve (AUC), F-measure and G-mean. Finally, based on AFG and coefficient of variation (CV), performance stability of standard classification algorithms on imbalanced network traffic data is obtained. Experiments of eight widely used standard classification algorithms on 25 different imbalanced network traffic data demonstrate that the classification performance of GNB, RF and DT is unstable, while BNB, KNN, LR, GBDT, and SVC are relatively stable and not susceptible to imbalanced data. Especially, the KNN has the most stable classification performance. Also, the results are statistically confirmed by Friedman and Nemenyi post hoc statistical tests. Imbalanced network traffic data (dpeaa)DE-He213 Data augmentation algorithms (dpeaa)DE-He213 Standard classification algorithms (dpeaa)DE-He213 Stable classification performance (dpeaa)DE-He213 Ma, Kai aut Wang, Fei aut Hu, Xiaowen aut Yu, Qingying aut Guo, Liangmin aut Chen, Fulong aut Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 1 vom: 26. Okt., Seite 217-234 (DE-627)SPR006469531 nnns volume:28 year:2023 number:1 day:26 month:10 pages:217-234 https://dx.doi.org/10.1007/s00500-023-09331-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 1 26 10 217-234 |
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10.1007/s00500-023-09331-1 doi (DE-627)SPR054258472 (SPR)s00500-023-09331-1-e DE-627 ger DE-627 rakwb eng Zheng, Ming verfasserin (orcid)0000-0001-9001-0859 aut Which standard classification algorithm has more stable performance for imbalanced network traffic data? 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Most standard classification algorithms are difficult to effectively learn and predict from imbalanced network traffic data, which usually leads to lower classification accuracy. To analyze the influence of imbalanced network traffic data on the performance of standard classification algorithms, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced network traffic data set with gradually varying Imbalance Ratio (IR) and belonging to the same distribution. Then, to obtain more objective classification result and simplify the evaluation process, the evaluation metric AFG is used to evaluate the classification performance of standard classification algorithms based on area under the receiver operating characteristic curve (AUC), F-measure and G-mean. Finally, based on AFG and coefficient of variation (CV), performance stability of standard classification algorithms on imbalanced network traffic data is obtained. Experiments of eight widely used standard classification algorithms on 25 different imbalanced network traffic data demonstrate that the classification performance of GNB, RF and DT is unstable, while BNB, KNN, LR, GBDT, and SVC are relatively stable and not susceptible to imbalanced data. Especially, the KNN has the most stable classification performance. Also, the results are statistically confirmed by Friedman and Nemenyi post hoc statistical tests. Imbalanced network traffic data (dpeaa)DE-He213 Data augmentation algorithms (dpeaa)DE-He213 Standard classification algorithms (dpeaa)DE-He213 Stable classification performance (dpeaa)DE-He213 Ma, Kai aut Wang, Fei aut Hu, Xiaowen aut Yu, Qingying aut Guo, Liangmin aut Chen, Fulong aut Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 1 vom: 26. Okt., Seite 217-234 (DE-627)SPR006469531 nnns volume:28 year:2023 number:1 day:26 month:10 pages:217-234 https://dx.doi.org/10.1007/s00500-023-09331-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 1 26 10 217-234 |
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10.1007/s00500-023-09331-1 doi (DE-627)SPR054258472 (SPR)s00500-023-09331-1-e DE-627 ger DE-627 rakwb eng Zheng, Ming verfasserin (orcid)0000-0001-9001-0859 aut Which standard classification algorithm has more stable performance for imbalanced network traffic data? 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Most standard classification algorithms are difficult to effectively learn and predict from imbalanced network traffic data, which usually leads to lower classification accuracy. To analyze the influence of imbalanced network traffic data on the performance of standard classification algorithms, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced network traffic data set with gradually varying Imbalance Ratio (IR) and belonging to the same distribution. Then, to obtain more objective classification result and simplify the evaluation process, the evaluation metric AFG is used to evaluate the classification performance of standard classification algorithms based on area under the receiver operating characteristic curve (AUC), F-measure and G-mean. Finally, based on AFG and coefficient of variation (CV), performance stability of standard classification algorithms on imbalanced network traffic data is obtained. Experiments of eight widely used standard classification algorithms on 25 different imbalanced network traffic data demonstrate that the classification performance of GNB, RF and DT is unstable, while BNB, KNN, LR, GBDT, and SVC are relatively stable and not susceptible to imbalanced data. Especially, the KNN has the most stable classification performance. Also, the results are statistically confirmed by Friedman and Nemenyi post hoc statistical tests. Imbalanced network traffic data (dpeaa)DE-He213 Data augmentation algorithms (dpeaa)DE-He213 Standard classification algorithms (dpeaa)DE-He213 Stable classification performance (dpeaa)DE-He213 Ma, Kai aut Wang, Fei aut Hu, Xiaowen aut Yu, Qingying aut Guo, Liangmin aut Chen, Fulong aut Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 1 vom: 26. Okt., Seite 217-234 (DE-627)SPR006469531 nnns volume:28 year:2023 number:1 day:26 month:10 pages:217-234 https://dx.doi.org/10.1007/s00500-023-09331-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 1 26 10 217-234 |
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10.1007/s00500-023-09331-1 doi (DE-627)SPR054258472 (SPR)s00500-023-09331-1-e DE-627 ger DE-627 rakwb eng Zheng, Ming verfasserin (orcid)0000-0001-9001-0859 aut Which standard classification algorithm has more stable performance for imbalanced network traffic data? 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Most standard classification algorithms are difficult to effectively learn and predict from imbalanced network traffic data, which usually leads to lower classification accuracy. To analyze the influence of imbalanced network traffic data on the performance of standard classification algorithms, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced network traffic data set with gradually varying Imbalance Ratio (IR) and belonging to the same distribution. Then, to obtain more objective classification result and simplify the evaluation process, the evaluation metric AFG is used to evaluate the classification performance of standard classification algorithms based on area under the receiver operating characteristic curve (AUC), F-measure and G-mean. Finally, based on AFG and coefficient of variation (CV), performance stability of standard classification algorithms on imbalanced network traffic data is obtained. Experiments of eight widely used standard classification algorithms on 25 different imbalanced network traffic data demonstrate that the classification performance of GNB, RF and DT is unstable, while BNB, KNN, LR, GBDT, and SVC are relatively stable and not susceptible to imbalanced data. Especially, the KNN has the most stable classification performance. Also, the results are statistically confirmed by Friedman and Nemenyi post hoc statistical tests. Imbalanced network traffic data (dpeaa)DE-He213 Data augmentation algorithms (dpeaa)DE-He213 Standard classification algorithms (dpeaa)DE-He213 Stable classification performance (dpeaa)DE-He213 Ma, Kai aut Wang, Fei aut Hu, Xiaowen aut Yu, Qingying aut Guo, Liangmin aut Chen, Fulong aut Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 1 vom: 26. Okt., Seite 217-234 (DE-627)SPR006469531 nnns volume:28 year:2023 number:1 day:26 month:10 pages:217-234 https://dx.doi.org/10.1007/s00500-023-09331-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 1 26 10 217-234 |
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Zheng, Ming Ma, Kai Wang, Fei Hu, Xiaowen Yu, Qingying Guo, Liangmin Chen, Fulong |
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Elektronische Aufsätze |
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Zheng, Ming |
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10.1007/s00500-023-09331-1 |
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which standard classification algorithm has more stable performance for imbalanced network traffic data? |
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Which standard classification algorithm has more stable performance for imbalanced network traffic data? |
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Abstract Most standard classification algorithms are difficult to effectively learn and predict from imbalanced network traffic data, which usually leads to lower classification accuracy. To analyze the influence of imbalanced network traffic data on the performance of standard classification algorithms, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced network traffic data set with gradually varying Imbalance Ratio (IR) and belonging to the same distribution. Then, to obtain more objective classification result and simplify the evaluation process, the evaluation metric AFG is used to evaluate the classification performance of standard classification algorithms based on area under the receiver operating characteristic curve (AUC), F-measure and G-mean. Finally, based on AFG and coefficient of variation (CV), performance stability of standard classification algorithms on imbalanced network traffic data is obtained. Experiments of eight widely used standard classification algorithms on 25 different imbalanced network traffic data demonstrate that the classification performance of GNB, RF and DT is unstable, while BNB, KNN, LR, GBDT, and SVC are relatively stable and not susceptible to imbalanced data. Especially, the KNN has the most stable classification performance. Also, the results are statistically confirmed by Friedman and Nemenyi post hoc statistical tests. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract Most standard classification algorithms are difficult to effectively learn and predict from imbalanced network traffic data, which usually leads to lower classification accuracy. To analyze the influence of imbalanced network traffic data on the performance of standard classification algorithms, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced network traffic data set with gradually varying Imbalance Ratio (IR) and belonging to the same distribution. Then, to obtain more objective classification result and simplify the evaluation process, the evaluation metric AFG is used to evaluate the classification performance of standard classification algorithms based on area under the receiver operating characteristic curve (AUC), F-measure and G-mean. Finally, based on AFG and coefficient of variation (CV), performance stability of standard classification algorithms on imbalanced network traffic data is obtained. Experiments of eight widely used standard classification algorithms on 25 different imbalanced network traffic data demonstrate that the classification performance of GNB, RF and DT is unstable, while BNB, KNN, LR, GBDT, and SVC are relatively stable and not susceptible to imbalanced data. Especially, the KNN has the most stable classification performance. Also, the results are statistically confirmed by Friedman and Nemenyi post hoc statistical tests. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Most standard classification algorithms are difficult to effectively learn and predict from imbalanced network traffic data, which usually leads to lower classification accuracy. To analyze the influence of imbalanced network traffic data on the performance of standard classification algorithms, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced network traffic data set with gradually varying Imbalance Ratio (IR) and belonging to the same distribution. Then, to obtain more objective classification result and simplify the evaluation process, the evaluation metric AFG is used to evaluate the classification performance of standard classification algorithms based on area under the receiver operating characteristic curve (AUC), F-measure and G-mean. Finally, based on AFG and coefficient of variation (CV), performance stability of standard classification algorithms on imbalanced network traffic data is obtained. Experiments of eight widely used standard classification algorithms on 25 different imbalanced network traffic data demonstrate that the classification performance of GNB, RF and DT is unstable, while BNB, KNN, LR, GBDT, and SVC are relatively stable and not susceptible to imbalanced data. Especially, the KNN has the most stable classification performance. Also, the results are statistically confirmed by Friedman and Nemenyi post hoc statistical tests. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Which standard classification algorithm has more stable performance for imbalanced network traffic data? |
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Ma, Kai Wang, Fei Hu, Xiaowen Yu, Qingying Guo, Liangmin Chen, Fulong |
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