ISMOTE: A More Accurate Alternative for SMOTE
Abstract Classification models trained on imbalanced datasets tend to be biased towards the majority category, resulting in reduced accuracy for minority categories. A common approach to address this problem is to generate artificial data for underrepresented categories. The Synthetic Minority Over-...
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| Autor*in: |
Song, Jiuxiang [verfasserIn] Liu, Jizhong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2024 |
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| Übergeordnetes Werk: |
Enthalten in: Neural processing letters - Springer US, 1994, 56(2024), 5 vom: 04. Okt. |
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| Übergeordnetes Werk: |
volume:56 ; year:2024 ; number:5 ; day:04 ; month:10 |
| Links: |
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| DOI / URN: |
10.1007/s11063-024-11695-w |
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| Katalog-ID: |
SPR057666601 |
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| 520 | |a Abstract Classification models trained on imbalanced datasets tend to be biased towards the majority category, resulting in reduced accuracy for minority categories. A common approach to address this problem is to generate artificial data for underrepresented categories. The Synthetic Minority Over-sampling Technique (SMOTE) algorithm and its variants are widely used for this purpose. In this paper, we propose a modification to the data generation mechanism called Iteration-based SMOTE (ISMOTE). Unlike SMOTE, the ISMOTE algorithm trains the data for multiple iterations. In each iteration, the model generates new samples in the vicinity of appropriately misclassified data. These new samples are then fed into the classification model, thus improving classification accuracy over the course of multiple iterations. We compare the performance of ISMOTE with SMOTE and other commonly used oversampling algorithms. Our empirical results demonstrate that ISMOTE significantly improves the quality of the generated data compared to other oversampling methods. Additionally, we conduct experiments to verify the effect of parameters on the model and provide suggestions for choosing appropriate values to improve performance. | ||
| 650 | 4 | |a Imbalanced datasets |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Synthetic minority over-sampling technique |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Iteration-based SMOTE |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Oversampling |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Liu, Jizhong |e verfasserin |4 aut | |
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10.1007/s11063-024-11695-w doi (DE-627)SPR057666601 (SPR)s11063-024-11695-w-e DE-627 ger DE-627 rakwb eng 000 VZ 54.72 bkl Song, Jiuxiang verfasserin (orcid)0000-0002-0173-6824 aut ISMOTE: A More Accurate Alternative for SMOTE 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Classification models trained on imbalanced datasets tend to be biased towards the majority category, resulting in reduced accuracy for minority categories. A common approach to address this problem is to generate artificial data for underrepresented categories. The Synthetic Minority Over-sampling Technique (SMOTE) algorithm and its variants are widely used for this purpose. In this paper, we propose a modification to the data generation mechanism called Iteration-based SMOTE (ISMOTE). Unlike SMOTE, the ISMOTE algorithm trains the data for multiple iterations. In each iteration, the model generates new samples in the vicinity of appropriately misclassified data. These new samples are then fed into the classification model, thus improving classification accuracy over the course of multiple iterations. We compare the performance of ISMOTE with SMOTE and other commonly used oversampling algorithms. Our empirical results demonstrate that ISMOTE significantly improves the quality of the generated data compared to other oversampling methods. Additionally, we conduct experiments to verify the effect of parameters on the model and provide suggestions for choosing appropriate values to improve performance. Imbalanced datasets (dpeaa)DE-He213 Synthetic minority over-sampling technique (dpeaa)DE-He213 Iteration-based SMOTE (dpeaa)DE-He213 Oversampling (dpeaa)DE-He213 Liu, Jizhong verfasserin aut Enthalten in Neural processing letters Springer US, 1994 56(2024), 5 vom: 04. Okt. (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:56 year:2024 number:5 day:04 month:10 https://dx.doi.org/10.1007/s11063-024-11695-w X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 54.72 VZ AR 56 2024 5 04 10 |
| spelling |
10.1007/s11063-024-11695-w doi (DE-627)SPR057666601 (SPR)s11063-024-11695-w-e DE-627 ger DE-627 rakwb eng 000 VZ 54.72 bkl Song, Jiuxiang verfasserin (orcid)0000-0002-0173-6824 aut ISMOTE: A More Accurate Alternative for SMOTE 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Classification models trained on imbalanced datasets tend to be biased towards the majority category, resulting in reduced accuracy for minority categories. A common approach to address this problem is to generate artificial data for underrepresented categories. The Synthetic Minority Over-sampling Technique (SMOTE) algorithm and its variants are widely used for this purpose. In this paper, we propose a modification to the data generation mechanism called Iteration-based SMOTE (ISMOTE). Unlike SMOTE, the ISMOTE algorithm trains the data for multiple iterations. In each iteration, the model generates new samples in the vicinity of appropriately misclassified data. These new samples are then fed into the classification model, thus improving classification accuracy over the course of multiple iterations. We compare the performance of ISMOTE with SMOTE and other commonly used oversampling algorithms. Our empirical results demonstrate that ISMOTE significantly improves the quality of the generated data compared to other oversampling methods. Additionally, we conduct experiments to verify the effect of parameters on the model and provide suggestions for choosing appropriate values to improve performance. Imbalanced datasets (dpeaa)DE-He213 Synthetic minority over-sampling technique (dpeaa)DE-He213 Iteration-based SMOTE (dpeaa)DE-He213 Oversampling (dpeaa)DE-He213 Liu, Jizhong verfasserin aut Enthalten in Neural processing letters Springer US, 1994 56(2024), 5 vom: 04. Okt. (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:56 year:2024 number:5 day:04 month:10 https://dx.doi.org/10.1007/s11063-024-11695-w X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 54.72 VZ AR 56 2024 5 04 10 |
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10.1007/s11063-024-11695-w doi (DE-627)SPR057666601 (SPR)s11063-024-11695-w-e DE-627 ger DE-627 rakwb eng 000 VZ 54.72 bkl Song, Jiuxiang verfasserin (orcid)0000-0002-0173-6824 aut ISMOTE: A More Accurate Alternative for SMOTE 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Classification models trained on imbalanced datasets tend to be biased towards the majority category, resulting in reduced accuracy for minority categories. A common approach to address this problem is to generate artificial data for underrepresented categories. The Synthetic Minority Over-sampling Technique (SMOTE) algorithm and its variants are widely used for this purpose. In this paper, we propose a modification to the data generation mechanism called Iteration-based SMOTE (ISMOTE). Unlike SMOTE, the ISMOTE algorithm trains the data for multiple iterations. In each iteration, the model generates new samples in the vicinity of appropriately misclassified data. These new samples are then fed into the classification model, thus improving classification accuracy over the course of multiple iterations. We compare the performance of ISMOTE with SMOTE and other commonly used oversampling algorithms. Our empirical results demonstrate that ISMOTE significantly improves the quality of the generated data compared to other oversampling methods. Additionally, we conduct experiments to verify the effect of parameters on the model and provide suggestions for choosing appropriate values to improve performance. Imbalanced datasets (dpeaa)DE-He213 Synthetic minority over-sampling technique (dpeaa)DE-He213 Iteration-based SMOTE (dpeaa)DE-He213 Oversampling (dpeaa)DE-He213 Liu, Jizhong verfasserin aut Enthalten in Neural processing letters Springer US, 1994 56(2024), 5 vom: 04. Okt. (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:56 year:2024 number:5 day:04 month:10 https://dx.doi.org/10.1007/s11063-024-11695-w X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 54.72 VZ AR 56 2024 5 04 10 |
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10.1007/s11063-024-11695-w doi (DE-627)SPR057666601 (SPR)s11063-024-11695-w-e DE-627 ger DE-627 rakwb eng 000 VZ 54.72 bkl Song, Jiuxiang verfasserin (orcid)0000-0002-0173-6824 aut ISMOTE: A More Accurate Alternative for SMOTE 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Classification models trained on imbalanced datasets tend to be biased towards the majority category, resulting in reduced accuracy for minority categories. A common approach to address this problem is to generate artificial data for underrepresented categories. The Synthetic Minority Over-sampling Technique (SMOTE) algorithm and its variants are widely used for this purpose. In this paper, we propose a modification to the data generation mechanism called Iteration-based SMOTE (ISMOTE). Unlike SMOTE, the ISMOTE algorithm trains the data for multiple iterations. In each iteration, the model generates new samples in the vicinity of appropriately misclassified data. These new samples are then fed into the classification model, thus improving classification accuracy over the course of multiple iterations. We compare the performance of ISMOTE with SMOTE and other commonly used oversampling algorithms. Our empirical results demonstrate that ISMOTE significantly improves the quality of the generated data compared to other oversampling methods. Additionally, we conduct experiments to verify the effect of parameters on the model and provide suggestions for choosing appropriate values to improve performance. Imbalanced datasets (dpeaa)DE-He213 Synthetic minority over-sampling technique (dpeaa)DE-He213 Iteration-based SMOTE (dpeaa)DE-He213 Oversampling (dpeaa)DE-He213 Liu, Jizhong verfasserin aut Enthalten in Neural processing letters Springer US, 1994 56(2024), 5 vom: 04. Okt. (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:56 year:2024 number:5 day:04 month:10 https://dx.doi.org/10.1007/s11063-024-11695-w X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 54.72 VZ AR 56 2024 5 04 10 |
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10.1007/s11063-024-11695-w doi (DE-627)SPR057666601 (SPR)s11063-024-11695-w-e DE-627 ger DE-627 rakwb eng 000 VZ 54.72 bkl Song, Jiuxiang verfasserin (orcid)0000-0002-0173-6824 aut ISMOTE: A More Accurate Alternative for SMOTE 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Classification models trained on imbalanced datasets tend to be biased towards the majority category, resulting in reduced accuracy for minority categories. A common approach to address this problem is to generate artificial data for underrepresented categories. The Synthetic Minority Over-sampling Technique (SMOTE) algorithm and its variants are widely used for this purpose. In this paper, we propose a modification to the data generation mechanism called Iteration-based SMOTE (ISMOTE). Unlike SMOTE, the ISMOTE algorithm trains the data for multiple iterations. In each iteration, the model generates new samples in the vicinity of appropriately misclassified data. These new samples are then fed into the classification model, thus improving classification accuracy over the course of multiple iterations. We compare the performance of ISMOTE with SMOTE and other commonly used oversampling algorithms. Our empirical results demonstrate that ISMOTE significantly improves the quality of the generated data compared to other oversampling methods. Additionally, we conduct experiments to verify the effect of parameters on the model and provide suggestions for choosing appropriate values to improve performance. Imbalanced datasets (dpeaa)DE-He213 Synthetic minority over-sampling technique (dpeaa)DE-He213 Iteration-based SMOTE (dpeaa)DE-He213 Oversampling (dpeaa)DE-He213 Liu, Jizhong verfasserin aut Enthalten in Neural processing letters Springer US, 1994 56(2024), 5 vom: 04. Okt. (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:56 year:2024 number:5 day:04 month:10 https://dx.doi.org/10.1007/s11063-024-11695-w X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 54.72 VZ AR 56 2024 5 04 10 |
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ismote: a more accurate alternative for smote |
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ISMOTE: A More Accurate Alternative for SMOTE |
| abstract |
Abstract Classification models trained on imbalanced datasets tend to be biased towards the majority category, resulting in reduced accuracy for minority categories. A common approach to address this problem is to generate artificial data for underrepresented categories. The Synthetic Minority Over-sampling Technique (SMOTE) algorithm and its variants are widely used for this purpose. In this paper, we propose a modification to the data generation mechanism called Iteration-based SMOTE (ISMOTE). Unlike SMOTE, the ISMOTE algorithm trains the data for multiple iterations. In each iteration, the model generates new samples in the vicinity of appropriately misclassified data. These new samples are then fed into the classification model, thus improving classification accuracy over the course of multiple iterations. We compare the performance of ISMOTE with SMOTE and other commonly used oversampling algorithms. Our empirical results demonstrate that ISMOTE significantly improves the quality of the generated data compared to other oversampling methods. Additionally, we conduct experiments to verify the effect of parameters on the model and provide suggestions for choosing appropriate values to improve performance. © The Author(s) 2024 |
| abstractGer |
Abstract Classification models trained on imbalanced datasets tend to be biased towards the majority category, resulting in reduced accuracy for minority categories. A common approach to address this problem is to generate artificial data for underrepresented categories. The Synthetic Minority Over-sampling Technique (SMOTE) algorithm and its variants are widely used for this purpose. In this paper, we propose a modification to the data generation mechanism called Iteration-based SMOTE (ISMOTE). Unlike SMOTE, the ISMOTE algorithm trains the data for multiple iterations. In each iteration, the model generates new samples in the vicinity of appropriately misclassified data. These new samples are then fed into the classification model, thus improving classification accuracy over the course of multiple iterations. We compare the performance of ISMOTE with SMOTE and other commonly used oversampling algorithms. Our empirical results demonstrate that ISMOTE significantly improves the quality of the generated data compared to other oversampling methods. Additionally, we conduct experiments to verify the effect of parameters on the model and provide suggestions for choosing appropriate values to improve performance. © The Author(s) 2024 |
| abstract_unstemmed |
Abstract Classification models trained on imbalanced datasets tend to be biased towards the majority category, resulting in reduced accuracy for minority categories. A common approach to address this problem is to generate artificial data for underrepresented categories. The Synthetic Minority Over-sampling Technique (SMOTE) algorithm and its variants are widely used for this purpose. In this paper, we propose a modification to the data generation mechanism called Iteration-based SMOTE (ISMOTE). Unlike SMOTE, the ISMOTE algorithm trains the data for multiple iterations. In each iteration, the model generates new samples in the vicinity of appropriately misclassified data. These new samples are then fed into the classification model, thus improving classification accuracy over the course of multiple iterations. We compare the performance of ISMOTE with SMOTE and other commonly used oversampling algorithms. Our empirical results demonstrate that ISMOTE significantly improves the quality of the generated data compared to other oversampling methods. Additionally, we conduct experiments to verify the effect of parameters on the model and provide suggestions for choosing appropriate values to improve performance. © The Author(s) 2024 |
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| title_short |
ISMOTE: A More Accurate Alternative for SMOTE |
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https://dx.doi.org/10.1007/s11063-024-11695-w |
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| author2 |
Liu, Jizhong |
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Liu, Jizhong |
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| doi_str |
10.1007/s11063-024-11695-w |
| up_date |
2024-11-01T06:37:13.722Z |
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| score |
7.1690454 |