Gaussian distribution resampling via Chebyshev distance for food computing
The problem of data imbalance often occurs in the real-world food domain. Traditional classification algorithms are prone to overfitting on imbalanced datasets, and the decision surface will be biased toward majority-class samples, making it difficult to identify minority-class samples. Although pre...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Tianle [verfasserIn] Zuo, Enguang [verfasserIn] Chen, Chen [verfasserIn] Chen, Cheng [verfasserIn] Zhong, Jie [verfasserIn] Yan, Junyi [verfasserIn] Lv, Xiaoyi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 150 |
|---|---|
| Übergeordnetes Werk: |
volume:150 |
| DOI / URN: |
10.1016/j.asoc.2023.111103 |
|---|
| Katalog-ID: |
ELV066316677 |
|---|
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| 520 | |a The problem of data imbalance often occurs in the real-world food domain. Traditional classification algorithms are prone to overfitting on imbalanced datasets, and the decision surface will be biased toward majority-class samples, making it difficult to identify minority-class samples. Although previous resampling techniques can deal with the imbalance problem by balancing the dataset, they may produce class overlap because the anchor samples are not appropriately selected and the generated dataset does not conform to the original distribution. This paper proposes an adaptive resampling technique based on Gaussian distribution oversampling combined with random undersampling (GDRS) to address the abovementioned problems. The technique is based on the Chebyshev distance combining the weight information of the minority-class samples to select a suitable anchor sample. A new dataset conforming to the original distribution is generated in the form of a Gaussian distribution around the anchor sample. Then the random undersampling technique is combined to reduce the possibility of overfitting. The technique is applied to five UCI datasets and compared with seven imbalanced learning methods. The experimental results demonstrate that our method GDRS yields optimal performance. We also validate the effectiveness of our method in dealing with real dairy datasets with different imbalance ratios, which is promising for application in the food field. | ||
| 650 | 4 | |a Food computing | |
| 650 | 4 | |a Imbalanced learning | |
| 650 | 4 | |a Gaussian distribution oversampling | |
| 650 | 4 | |a Random undersampling | |
| 650 | 4 | |a Chebyshev distance | |
| 700 | 1 | |a Zuo, Enguang |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Chen |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Cheng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhong, Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Junyi |e verfasserin |4 aut | |
| 700 | 1 | |a Lv, Xiaoyi |e verfasserin |4 aut | |
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10.1016/j.asoc.2023.111103 doi (DE-627)ELV066316677 (ELSEVIER)S1568-4946(23)01121-3 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Li, Tianle verfasserin aut Gaussian distribution resampling via Chebyshev distance for food computing 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The problem of data imbalance often occurs in the real-world food domain. Traditional classification algorithms are prone to overfitting on imbalanced datasets, and the decision surface will be biased toward majority-class samples, making it difficult to identify minority-class samples. Although previous resampling techniques can deal with the imbalance problem by balancing the dataset, they may produce class overlap because the anchor samples are not appropriately selected and the generated dataset does not conform to the original distribution. This paper proposes an adaptive resampling technique based on Gaussian distribution oversampling combined with random undersampling (GDRS) to address the abovementioned problems. The technique is based on the Chebyshev distance combining the weight information of the minority-class samples to select a suitable anchor sample. A new dataset conforming to the original distribution is generated in the form of a Gaussian distribution around the anchor sample. Then the random undersampling technique is combined to reduce the possibility of overfitting. The technique is applied to five UCI datasets and compared with seven imbalanced learning methods. The experimental results demonstrate that our method GDRS yields optimal performance. We also validate the effectiveness of our method in dealing with real dairy datasets with different imbalance ratios, which is promising for application in the food field. Food computing Imbalanced learning Gaussian distribution oversampling Random undersampling Chebyshev distance Zuo, Enguang verfasserin aut Chen, Chen verfasserin aut Chen, Cheng verfasserin aut Zhong, Jie verfasserin aut Yan, Junyi verfasserin aut Lv, Xiaoyi verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 150 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:150 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ AR 150 |
| spelling |
10.1016/j.asoc.2023.111103 doi (DE-627)ELV066316677 (ELSEVIER)S1568-4946(23)01121-3 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Li, Tianle verfasserin aut Gaussian distribution resampling via Chebyshev distance for food computing 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The problem of data imbalance often occurs in the real-world food domain. Traditional classification algorithms are prone to overfitting on imbalanced datasets, and the decision surface will be biased toward majority-class samples, making it difficult to identify minority-class samples. Although previous resampling techniques can deal with the imbalance problem by balancing the dataset, they may produce class overlap because the anchor samples are not appropriately selected and the generated dataset does not conform to the original distribution. This paper proposes an adaptive resampling technique based on Gaussian distribution oversampling combined with random undersampling (GDRS) to address the abovementioned problems. The technique is based on the Chebyshev distance combining the weight information of the minority-class samples to select a suitable anchor sample. A new dataset conforming to the original distribution is generated in the form of a Gaussian distribution around the anchor sample. Then the random undersampling technique is combined to reduce the possibility of overfitting. The technique is applied to five UCI datasets and compared with seven imbalanced learning methods. The experimental results demonstrate that our method GDRS yields optimal performance. We also validate the effectiveness of our method in dealing with real dairy datasets with different imbalance ratios, which is promising for application in the food field. Food computing Imbalanced learning Gaussian distribution oversampling Random undersampling Chebyshev distance Zuo, Enguang verfasserin aut Chen, Chen verfasserin aut Chen, Cheng verfasserin aut Zhong, Jie verfasserin aut Yan, Junyi verfasserin aut Lv, Xiaoyi verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 150 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:150 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ AR 150 |
| allfields_unstemmed |
10.1016/j.asoc.2023.111103 doi (DE-627)ELV066316677 (ELSEVIER)S1568-4946(23)01121-3 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Li, Tianle verfasserin aut Gaussian distribution resampling via Chebyshev distance for food computing 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The problem of data imbalance often occurs in the real-world food domain. Traditional classification algorithms are prone to overfitting on imbalanced datasets, and the decision surface will be biased toward majority-class samples, making it difficult to identify minority-class samples. Although previous resampling techniques can deal with the imbalance problem by balancing the dataset, they may produce class overlap because the anchor samples are not appropriately selected and the generated dataset does not conform to the original distribution. This paper proposes an adaptive resampling technique based on Gaussian distribution oversampling combined with random undersampling (GDRS) to address the abovementioned problems. The technique is based on the Chebyshev distance combining the weight information of the minority-class samples to select a suitable anchor sample. A new dataset conforming to the original distribution is generated in the form of a Gaussian distribution around the anchor sample. Then the random undersampling technique is combined to reduce the possibility of overfitting. The technique is applied to five UCI datasets and compared with seven imbalanced learning methods. The experimental results demonstrate that our method GDRS yields optimal performance. We also validate the effectiveness of our method in dealing with real dairy datasets with different imbalance ratios, which is promising for application in the food field. Food computing Imbalanced learning Gaussian distribution oversampling Random undersampling Chebyshev distance Zuo, Enguang verfasserin aut Chen, Chen verfasserin aut Chen, Cheng verfasserin aut Zhong, Jie verfasserin aut Yan, Junyi verfasserin aut Lv, Xiaoyi verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 150 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:150 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ AR 150 |
| allfieldsGer |
10.1016/j.asoc.2023.111103 doi (DE-627)ELV066316677 (ELSEVIER)S1568-4946(23)01121-3 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Li, Tianle verfasserin aut Gaussian distribution resampling via Chebyshev distance for food computing 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The problem of data imbalance often occurs in the real-world food domain. Traditional classification algorithms are prone to overfitting on imbalanced datasets, and the decision surface will be biased toward majority-class samples, making it difficult to identify minority-class samples. Although previous resampling techniques can deal with the imbalance problem by balancing the dataset, they may produce class overlap because the anchor samples are not appropriately selected and the generated dataset does not conform to the original distribution. This paper proposes an adaptive resampling technique based on Gaussian distribution oversampling combined with random undersampling (GDRS) to address the abovementioned problems. The technique is based on the Chebyshev distance combining the weight information of the minority-class samples to select a suitable anchor sample. A new dataset conforming to the original distribution is generated in the form of a Gaussian distribution around the anchor sample. Then the random undersampling technique is combined to reduce the possibility of overfitting. The technique is applied to five UCI datasets and compared with seven imbalanced learning methods. The experimental results demonstrate that our method GDRS yields optimal performance. We also validate the effectiveness of our method in dealing with real dairy datasets with different imbalance ratios, which is promising for application in the food field. Food computing Imbalanced learning Gaussian distribution oversampling Random undersampling Chebyshev distance Zuo, Enguang verfasserin aut Chen, Chen verfasserin aut Chen, Cheng verfasserin aut Zhong, Jie verfasserin aut Yan, Junyi verfasserin aut Lv, Xiaoyi verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 150 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:150 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ AR 150 |
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10.1016/j.asoc.2023.111103 doi (DE-627)ELV066316677 (ELSEVIER)S1568-4946(23)01121-3 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Li, Tianle verfasserin aut Gaussian distribution resampling via Chebyshev distance for food computing 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The problem of data imbalance often occurs in the real-world food domain. Traditional classification algorithms are prone to overfitting on imbalanced datasets, and the decision surface will be biased toward majority-class samples, making it difficult to identify minority-class samples. Although previous resampling techniques can deal with the imbalance problem by balancing the dataset, they may produce class overlap because the anchor samples are not appropriately selected and the generated dataset does not conform to the original distribution. This paper proposes an adaptive resampling technique based on Gaussian distribution oversampling combined with random undersampling (GDRS) to address the abovementioned problems. The technique is based on the Chebyshev distance combining the weight information of the minority-class samples to select a suitable anchor sample. A new dataset conforming to the original distribution is generated in the form of a Gaussian distribution around the anchor sample. Then the random undersampling technique is combined to reduce the possibility of overfitting. The technique is applied to five UCI datasets and compared with seven imbalanced learning methods. The experimental results demonstrate that our method GDRS yields optimal performance. We also validate the effectiveness of our method in dealing with real dairy datasets with different imbalance ratios, which is promising for application in the food field. Food computing Imbalanced learning Gaussian distribution oversampling Random undersampling Chebyshev distance Zuo, Enguang verfasserin aut Chen, Chen verfasserin aut Chen, Cheng verfasserin aut Zhong, Jie verfasserin aut Yan, Junyi verfasserin aut Lv, Xiaoyi verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 150 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:150 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ AR 150 |
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Li, Tianle @@aut@@ Zuo, Enguang @@aut@@ Chen, Chen @@aut@@ Chen, Cheng @@aut@@ Zhong, Jie @@aut@@ Yan, Junyi @@aut@@ Lv, Xiaoyi @@aut@@ |
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004 VZ 54.00 bkl Gaussian distribution resampling via Chebyshev distance for food computing Food computing Imbalanced learning Gaussian distribution oversampling Random undersampling Chebyshev distance |
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gaussian distribution resampling via chebyshev distance for food computing |
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Gaussian distribution resampling via Chebyshev distance for food computing |
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The problem of data imbalance often occurs in the real-world food domain. Traditional classification algorithms are prone to overfitting on imbalanced datasets, and the decision surface will be biased toward majority-class samples, making it difficult to identify minority-class samples. Although previous resampling techniques can deal with the imbalance problem by balancing the dataset, they may produce class overlap because the anchor samples are not appropriately selected and the generated dataset does not conform to the original distribution. This paper proposes an adaptive resampling technique based on Gaussian distribution oversampling combined with random undersampling (GDRS) to address the abovementioned problems. The technique is based on the Chebyshev distance combining the weight information of the minority-class samples to select a suitable anchor sample. A new dataset conforming to the original distribution is generated in the form of a Gaussian distribution around the anchor sample. Then the random undersampling technique is combined to reduce the possibility of overfitting. The technique is applied to five UCI datasets and compared with seven imbalanced learning methods. The experimental results demonstrate that our method GDRS yields optimal performance. We also validate the effectiveness of our method in dealing with real dairy datasets with different imbalance ratios, which is promising for application in the food field. |
| abstractGer |
The problem of data imbalance often occurs in the real-world food domain. Traditional classification algorithms are prone to overfitting on imbalanced datasets, and the decision surface will be biased toward majority-class samples, making it difficult to identify minority-class samples. Although previous resampling techniques can deal with the imbalance problem by balancing the dataset, they may produce class overlap because the anchor samples are not appropriately selected and the generated dataset does not conform to the original distribution. This paper proposes an adaptive resampling technique based on Gaussian distribution oversampling combined with random undersampling (GDRS) to address the abovementioned problems. The technique is based on the Chebyshev distance combining the weight information of the minority-class samples to select a suitable anchor sample. A new dataset conforming to the original distribution is generated in the form of a Gaussian distribution around the anchor sample. Then the random undersampling technique is combined to reduce the possibility of overfitting. The technique is applied to five UCI datasets and compared with seven imbalanced learning methods. The experimental results demonstrate that our method GDRS yields optimal performance. We also validate the effectiveness of our method in dealing with real dairy datasets with different imbalance ratios, which is promising for application in the food field. |
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The problem of data imbalance often occurs in the real-world food domain. Traditional classification algorithms are prone to overfitting on imbalanced datasets, and the decision surface will be biased toward majority-class samples, making it difficult to identify minority-class samples. Although previous resampling techniques can deal with the imbalance problem by balancing the dataset, they may produce class overlap because the anchor samples are not appropriately selected and the generated dataset does not conform to the original distribution. This paper proposes an adaptive resampling technique based on Gaussian distribution oversampling combined with random undersampling (GDRS) to address the abovementioned problems. The technique is based on the Chebyshev distance combining the weight information of the minority-class samples to select a suitable anchor sample. A new dataset conforming to the original distribution is generated in the form of a Gaussian distribution around the anchor sample. Then the random undersampling technique is combined to reduce the possibility of overfitting. The technique is applied to five UCI datasets and compared with seven imbalanced learning methods. The experimental results demonstrate that our method GDRS yields optimal performance. We also validate the effectiveness of our method in dealing with real dairy datasets with different imbalance ratios, which is promising for application in the food field. |
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