Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification▪

In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose an adaptive SV-Borderline SMOTE-SVM (Synthetic Minority Oversampling Technique-Support Vector Machine) algorithm, specifically designed to overcome the challenges associated with imba...
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Autor*in:	Guo, Jiaqi [verfasserIn] Wu, Haiyan [verfasserIn] Chen, Xiaolei [verfasserIn] Lin, Weiguo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Imbalanced data classification Synthetic minority oversampling technique (SMOTE) Kernel space Support Vector Machine

Übergeordnetes Werk:	Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 150
Übergeordnetes Werk:	volume:150

DOI / URN:	10.1016/j.asoc.2023.110986

Katalog-ID:	ELV066316103

Internformat


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245	1	0	\|a Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification▪
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520			\|a In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose an adaptive SV-Borderline SMOTE-SVM (Synthetic Minority Oversampling Technique-Support Vector Machine) algorithm, specifically designed to overcome the challenges associated with imbalanced data classification. The algorithm begins by mapping the dataset into the kernel space using SVM to identify the class boundary samples, known as support vectors (SVs). Subsequently, the neighbors of positive sample’s support vector (SV+) are calculated based on the kernel distance. Based on the class distribution of these neighbors, the SV+ samples are labeled as either “concave” or “convex”. Based on these labels, new samples are adaptively generated using two distinct calculation approaches for different labeled SV+ samples. To construct the SVM decision function without requiring the explicit expression of new samples in the kernel space, a Gram matrix is designed. Notably, all the processes ensure the credibility and reliability of the new samples. Additionally, the adaptive interpolation approach helps to ensure the security and diversity of new samples. Extensive experiments were conducted on a set of 50 KEEL datasets to evaluate the performance of our proposed method for imbalanced data classification. In experiments, our method achieved the highest G-mean score in 33 datasets and the highest F-values in 32 datasets. These results highlight the effectiveness and superiority of our proposed method compared to other approaches in addressing the challenges of imbalanced data classification.
650		4	\|a Imbalanced data classification
650		4	\|a Synthetic minority oversampling technique (SMOTE)
650		4	\|a Kernel space
650		4	\|a Support Vector Machine
700	1		\|a Wu, Haiyan \|e verfasserin \|4 aut
700	1		\|a Chen, Xiaolei \|e verfasserin \|4 aut
700	1		\|a Lin, Weiguo \|e verfasserin \|4 aut
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allfields	10.1016/j.asoc.2023.110986 doi (DE-627)ELV066316103 (ELSEVIER)S1568-4946(23)01004-9 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Guo, Jiaqi verfasserin aut Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification▪ 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose an adaptive SV-Borderline SMOTE-SVM (Synthetic Minority Oversampling Technique-Support Vector Machine) algorithm, specifically designed to overcome the challenges associated with imbalanced data classification. The algorithm begins by mapping the dataset into the kernel space using SVM to identify the class boundary samples, known as support vectors (SVs). Subsequently, the neighbors of positive sample’s support vector (SV+) are calculated based on the kernel distance. Based on the class distribution of these neighbors, the SV+ samples are labeled as either “concave” or “convex”. Based on these labels, new samples are adaptively generated using two distinct calculation approaches for different labeled SV+ samples. To construct the SVM decision function without requiring the explicit expression of new samples in the kernel space, a Gram matrix is designed. Notably, all the processes ensure the credibility and reliability of the new samples. Additionally, the adaptive interpolation approach helps to ensure the security and diversity of new samples. Extensive experiments were conducted on a set of 50 KEEL datasets to evaluate the performance of our proposed method for imbalanced data classification. In experiments, our method achieved the highest G-mean score in 33 datasets and the highest F-values in 32 datasets. These results highlight the effectiveness and superiority of our proposed method compared to other approaches in addressing the challenges of imbalanced data classification. Imbalanced data classification Synthetic minority oversampling technique (SMOTE) Kernel space Support Vector Machine Wu, Haiyan verfasserin aut Chen, Xiaolei verfasserin aut Lin, Weiguo 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.2023.110986 doi (DE-627)ELV066316103 (ELSEVIER)S1568-4946(23)01004-9 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Guo, Jiaqi verfasserin aut Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification▪ 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose an adaptive SV-Borderline SMOTE-SVM (Synthetic Minority Oversampling Technique-Support Vector Machine) algorithm, specifically designed to overcome the challenges associated with imbalanced data classification. The algorithm begins by mapping the dataset into the kernel space using SVM to identify the class boundary samples, known as support vectors (SVs). Subsequently, the neighbors of positive sample’s support vector (SV+) are calculated based on the kernel distance. Based on the class distribution of these neighbors, the SV+ samples are labeled as either “concave” or “convex”. Based on these labels, new samples are adaptively generated using two distinct calculation approaches for different labeled SV+ samples. To construct the SVM decision function without requiring the explicit expression of new samples in the kernel space, a Gram matrix is designed. Notably, all the processes ensure the credibility and reliability of the new samples. Additionally, the adaptive interpolation approach helps to ensure the security and diversity of new samples. Extensive experiments were conducted on a set of 50 KEEL datasets to evaluate the performance of our proposed method for imbalanced data classification. In experiments, our method achieved the highest G-mean score in 33 datasets and the highest F-values in 32 datasets. These results highlight the effectiveness and superiority of our proposed method compared to other approaches in addressing the challenges of imbalanced data classification. Imbalanced data classification Synthetic minority oversampling technique (SMOTE) Kernel space Support Vector Machine Wu, Haiyan verfasserin aut Chen, Xiaolei verfasserin aut Lin, Weiguo 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.2023.110986 doi (DE-627)ELV066316103 (ELSEVIER)S1568-4946(23)01004-9 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Guo, Jiaqi verfasserin aut Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification▪ 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose an adaptive SV-Borderline SMOTE-SVM (Synthetic Minority Oversampling Technique-Support Vector Machine) algorithm, specifically designed to overcome the challenges associated with imbalanced data classification. The algorithm begins by mapping the dataset into the kernel space using SVM to identify the class boundary samples, known as support vectors (SVs). Subsequently, the neighbors of positive sample’s support vector (SV+) are calculated based on the kernel distance. Based on the class distribution of these neighbors, the SV+ samples are labeled as either “concave” or “convex”. Based on these labels, new samples are adaptively generated using two distinct calculation approaches for different labeled SV+ samples. To construct the SVM decision function without requiring the explicit expression of new samples in the kernel space, a Gram matrix is designed. Notably, all the processes ensure the credibility and reliability of the new samples. Additionally, the adaptive interpolation approach helps to ensure the security and diversity of new samples. Extensive experiments were conducted on a set of 50 KEEL datasets to evaluate the performance of our proposed method for imbalanced data classification. In experiments, our method achieved the highest G-mean score in 33 datasets and the highest F-values in 32 datasets. These results highlight the effectiveness and superiority of our proposed method compared to other approaches in addressing the challenges of imbalanced data classification. Imbalanced data classification Synthetic minority oversampling technique (SMOTE) Kernel space Support Vector Machine Wu, Haiyan verfasserin aut Chen, Xiaolei verfasserin aut Lin, Weiguo 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.2023.110986 doi (DE-627)ELV066316103 (ELSEVIER)S1568-4946(23)01004-9 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Guo, Jiaqi verfasserin aut Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification▪ 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose an adaptive SV-Borderline SMOTE-SVM (Synthetic Minority Oversampling Technique-Support Vector Machine) algorithm, specifically designed to overcome the challenges associated with imbalanced data classification. The algorithm begins by mapping the dataset into the kernel space using SVM to identify the class boundary samples, known as support vectors (SVs). Subsequently, the neighbors of positive sample’s support vector (SV+) are calculated based on the kernel distance. Based on the class distribution of these neighbors, the SV+ samples are labeled as either “concave” or “convex”. Based on these labels, new samples are adaptively generated using two distinct calculation approaches for different labeled SV+ samples. To construct the SVM decision function without requiring the explicit expression of new samples in the kernel space, a Gram matrix is designed. Notably, all the processes ensure the credibility and reliability of the new samples. Additionally, the adaptive interpolation approach helps to ensure the security and diversity of new samples. Extensive experiments were conducted on a set of 50 KEEL datasets to evaluate the performance of our proposed method for imbalanced data classification. In experiments, our method achieved the highest G-mean score in 33 datasets and the highest F-values in 32 datasets. These results highlight the effectiveness and superiority of our proposed method compared to other approaches in addressing the challenges of imbalanced data classification. Imbalanced data classification Synthetic minority oversampling technique (SMOTE) Kernel space Support Vector Machine Wu, Haiyan verfasserin aut Chen, Xiaolei verfasserin aut Lin, Weiguo 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
allfieldsSound	10.1016/j.asoc.2023.110986 doi (DE-627)ELV066316103 (ELSEVIER)S1568-4946(23)01004-9 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Guo, Jiaqi verfasserin aut Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification▪ 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose an adaptive SV-Borderline SMOTE-SVM (Synthetic Minority Oversampling Technique-Support Vector Machine) algorithm, specifically designed to overcome the challenges associated with imbalanced data classification. The algorithm begins by mapping the dataset into the kernel space using SVM to identify the class boundary samples, known as support vectors (SVs). Subsequently, the neighbors of positive sample’s support vector (SV+) are calculated based on the kernel distance. Based on the class distribution of these neighbors, the SV+ samples are labeled as either “concave” or “convex”. Based on these labels, new samples are adaptively generated using two distinct calculation approaches for different labeled SV+ samples. To construct the SVM decision function without requiring the explicit expression of new samples in the kernel space, a Gram matrix is designed. Notably, all the processes ensure the credibility and reliability of the new samples. Additionally, the adaptive interpolation approach helps to ensure the security and diversity of new samples. Extensive experiments were conducted on a set of 50 KEEL datasets to evaluate the performance of our proposed method for imbalanced data classification. In experiments, our method achieved the highest G-mean score in 33 datasets and the highest F-values in 32 datasets. These results highlight the effectiveness and superiority of our proposed method compared to other approaches in addressing the challenges of imbalanced data classification. Imbalanced data classification Synthetic minority oversampling technique (SMOTE) Kernel space Support Vector Machine Wu, Haiyan verfasserin aut Chen, Xiaolei verfasserin aut Lin, Weiguo 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
language	English
source	Enthalten in Applied soft computing 150 volume:150
sourceStr	Enthalten in Applied soft computing 150 volume:150
format_phy_str_mv	Article
bklname	Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	Imbalanced data classification Synthetic minority oversampling technique (SMOTE) Kernel space Support Vector Machine
dewey-raw	004
isfreeaccess_bool	false
container_title	Applied soft computing
authorswithroles_txt_mv	Guo, Jiaqi @@aut@@ Wu, Haiyan @@aut@@ Chen, Xiaolei @@aut@@ Lin, Weiguo @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	334375754
dewey-sort	14
id	ELV066316103
language_de	englisch
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author	Guo, Jiaqi
spellingShingle	Guo, Jiaqi ddc 004 bkl 54.00 misc Imbalanced data classification misc Synthetic minority oversampling technique (SMOTE) misc Kernel space misc Support Vector Machine Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification▪
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topic_title	004 VZ 54.00 bkl Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification▪ Imbalanced data classification Synthetic minority oversampling technique (SMOTE) Kernel space Support Vector Machine
topic	ddc 004 bkl 54.00 misc Imbalanced data classification misc Synthetic minority oversampling technique (SMOTE) misc Kernel space misc Support Vector Machine
topic_unstemmed	ddc 004 bkl 54.00 misc Imbalanced data classification misc Synthetic minority oversampling technique (SMOTE) misc Kernel space misc Support Vector Machine
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title	Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification▪
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title_full	Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification▪
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author_browse	Guo, Jiaqi Wu, Haiyan Chen, Xiaolei Lin, Weiguo
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title_sort	adaptive sv-borderline smote-svm algorithm for imbalanced data classification▪
title_auth	Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification▪
abstract	In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose an adaptive SV-Borderline SMOTE-SVM (Synthetic Minority Oversampling Technique-Support Vector Machine) algorithm, specifically designed to overcome the challenges associated with imbalanced data classification. The algorithm begins by mapping the dataset into the kernel space using SVM to identify the class boundary samples, known as support vectors (SVs). Subsequently, the neighbors of positive sample’s support vector (SV+) are calculated based on the kernel distance. Based on the class distribution of these neighbors, the SV+ samples are labeled as either “concave” or “convex”. Based on these labels, new samples are adaptively generated using two distinct calculation approaches for different labeled SV+ samples. To construct the SVM decision function without requiring the explicit expression of new samples in the kernel space, a Gram matrix is designed. Notably, all the processes ensure the credibility and reliability of the new samples. Additionally, the adaptive interpolation approach helps to ensure the security and diversity of new samples. Extensive experiments were conducted on a set of 50 KEEL datasets to evaluate the performance of our proposed method for imbalanced data classification. In experiments, our method achieved the highest G-mean score in 33 datasets and the highest F-values in 32 datasets. These results highlight the effectiveness and superiority of our proposed method compared to other approaches in addressing the challenges of imbalanced data classification.
abstractGer	In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose an adaptive SV-Borderline SMOTE-SVM (Synthetic Minority Oversampling Technique-Support Vector Machine) algorithm, specifically designed to overcome the challenges associated with imbalanced data classification. The algorithm begins by mapping the dataset into the kernel space using SVM to identify the class boundary samples, known as support vectors (SVs). Subsequently, the neighbors of positive sample’s support vector (SV+) are calculated based on the kernel distance. Based on the class distribution of these neighbors, the SV+ samples are labeled as either “concave” or “convex”. Based on these labels, new samples are adaptively generated using two distinct calculation approaches for different labeled SV+ samples. To construct the SVM decision function without requiring the explicit expression of new samples in the kernel space, a Gram matrix is designed. Notably, all the processes ensure the credibility and reliability of the new samples. Additionally, the adaptive interpolation approach helps to ensure the security and diversity of new samples. Extensive experiments were conducted on a set of 50 KEEL datasets to evaluate the performance of our proposed method for imbalanced data classification. In experiments, our method achieved the highest G-mean score in 33 datasets and the highest F-values in 32 datasets. These results highlight the effectiveness and superiority of our proposed method compared to other approaches in addressing the challenges of imbalanced data classification.
abstract_unstemmed	In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose an adaptive SV-Borderline SMOTE-SVM (Synthetic Minority Oversampling Technique-Support Vector Machine) algorithm, specifically designed to overcome the challenges associated with imbalanced data classification. The algorithm begins by mapping the dataset into the kernel space using SVM to identify the class boundary samples, known as support vectors (SVs). Subsequently, the neighbors of positive sample’s support vector (SV+) are calculated based on the kernel distance. Based on the class distribution of these neighbors, the SV+ samples are labeled as either “concave” or “convex”. Based on these labels, new samples are adaptively generated using two distinct calculation approaches for different labeled SV+ samples. To construct the SVM decision function without requiring the explicit expression of new samples in the kernel space, a Gram matrix is designed. Notably, all the processes ensure the credibility and reliability of the new samples. Additionally, the adaptive interpolation approach helps to ensure the security and diversity of new samples. Extensive experiments were conducted on a set of 50 KEEL datasets to evaluate the performance of our proposed method for imbalanced data classification. In experiments, our method achieved the highest G-mean score in 33 datasets and the highest F-values in 32 datasets. These results highlight the effectiveness and superiority of our proposed method compared to other approaches in addressing the challenges of imbalanced data classification.
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title_short	Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification▪
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author2	Wu, Haiyan Chen, Xiaolei Lin, Weiguo
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Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification▪

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