OUBoost: boosting based over and under sampling technique for handling imbalanced data
Abstract Most real-world datasets usually contain imbalanced data. Learning from datasets where the number of samples in one class (minority) is much smaller than in another class (majority) creates biased classifiers to the majority class. The overall prediction accuracy in imbalanced datasets is h...
Ausführliche Beschreibung
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| Autor*in: |
Mostafaei, Sahar Hassanzadeh [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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| 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: International journal of machine learning and cybernetics - Heidelberg : Springer, 2010, 14(2023), 10 vom: 10. Mai, Seite 3393-3411 |
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| Übergeordnetes Werk: |
volume:14 ; year:2023 ; number:10 ; day:10 ; month:05 ; pages:3393-3411 |
| Links: |
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| DOI / URN: |
10.1007/s13042-023-01839-0 |
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| Katalog-ID: |
SPR052831086 |
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| 520 | |a Abstract Most real-world datasets usually contain imbalanced data. Learning from datasets where the number of samples in one class (minority) is much smaller than in another class (majority) creates biased classifiers to the majority class. The overall prediction accuracy in imbalanced datasets is higher than 90%, while this accuracy is relatively lower for minority classes. In this paper, we first propose a new technique for under-sampling based on the Peak clustering method from the majority class on imbalanced datasets. We then propose a novel boosting-based algorithm for learning from imbalanced datasets, based on a combination of the proposed Peak under-sampling algorithm and over-sampling technique (SMOTE) in the boosting procedure, named OUBoost. In the proposed OUBoost algorithm, misclassified examples are not given equal weights. OUBoost selects useful examples from the majority class and creates synthetic examples for the minority class. In fact, it indirectly updates the weights of samples. We designed experiments using several evaluation metrics, such as Recall, MCC, Gmean, and F-score on 30 real-world imbalanced datasets. The results show improved prediction performance in the minority class in most used datasets using OUBoost. We further report time comparisons and statistical tests to analyze our proposed algorithm in more details. | ||
| 650 | 4 | |a Imbalanced data classification |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Class imbalanced problem |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Over-sampling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Under-sampling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Imbalance ratio |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Boosting |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Tanha, Jafar |0 (orcid)0000-0002-0779-6027 |4 aut | |
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10.1007/s13042-023-01839-0 doi (DE-627)SPR052831086 (SPR)s13042-023-01839-0-e DE-627 ger DE-627 rakwb eng Mostafaei, Sahar Hassanzadeh verfasserin aut OUBoost: boosting based over and under sampling technique for handling imbalanced 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 real-world datasets usually contain imbalanced data. Learning from datasets where the number of samples in one class (minority) is much smaller than in another class (majority) creates biased classifiers to the majority class. The overall prediction accuracy in imbalanced datasets is higher than 90%, while this accuracy is relatively lower for minority classes. In this paper, we first propose a new technique for under-sampling based on the Peak clustering method from the majority class on imbalanced datasets. We then propose a novel boosting-based algorithm for learning from imbalanced datasets, based on a combination of the proposed Peak under-sampling algorithm and over-sampling technique (SMOTE) in the boosting procedure, named OUBoost. In the proposed OUBoost algorithm, misclassified examples are not given equal weights. OUBoost selects useful examples from the majority class and creates synthetic examples for the minority class. In fact, it indirectly updates the weights of samples. We designed experiments using several evaluation metrics, such as Recall, MCC, Gmean, and F-score on 30 real-world imbalanced datasets. The results show improved prediction performance in the minority class in most used datasets using OUBoost. We further report time comparisons and statistical tests to analyze our proposed algorithm in more details. Imbalanced data classification (dpeaa)DE-He213 Class imbalanced problem (dpeaa)DE-He213 Over-sampling (dpeaa)DE-He213 Under-sampling (dpeaa)DE-He213 Imbalance ratio (dpeaa)DE-He213 Boosting (dpeaa)DE-He213 Tanha, Jafar (orcid)0000-0002-0779-6027 aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 14(2023), 10 vom: 10. Mai, Seite 3393-3411 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:14 year:2023 number:10 day:10 month:05 pages:3393-3411 https://dx.doi.org/10.1007/s13042-023-01839-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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_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_2119 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_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 10 10 05 3393-3411 |
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10.1007/s13042-023-01839-0 doi (DE-627)SPR052831086 (SPR)s13042-023-01839-0-e DE-627 ger DE-627 rakwb eng Mostafaei, Sahar Hassanzadeh verfasserin aut OUBoost: boosting based over and under sampling technique for handling imbalanced 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 real-world datasets usually contain imbalanced data. Learning from datasets where the number of samples in one class (minority) is much smaller than in another class (majority) creates biased classifiers to the majority class. The overall prediction accuracy in imbalanced datasets is higher than 90%, while this accuracy is relatively lower for minority classes. In this paper, we first propose a new technique for under-sampling based on the Peak clustering method from the majority class on imbalanced datasets. We then propose a novel boosting-based algorithm for learning from imbalanced datasets, based on a combination of the proposed Peak under-sampling algorithm and over-sampling technique (SMOTE) in the boosting procedure, named OUBoost. In the proposed OUBoost algorithm, misclassified examples are not given equal weights. OUBoost selects useful examples from the majority class and creates synthetic examples for the minority class. In fact, it indirectly updates the weights of samples. We designed experiments using several evaluation metrics, such as Recall, MCC, Gmean, and F-score on 30 real-world imbalanced datasets. The results show improved prediction performance in the minority class in most used datasets using OUBoost. We further report time comparisons and statistical tests to analyze our proposed algorithm in more details. Imbalanced data classification (dpeaa)DE-He213 Class imbalanced problem (dpeaa)DE-He213 Over-sampling (dpeaa)DE-He213 Under-sampling (dpeaa)DE-He213 Imbalance ratio (dpeaa)DE-He213 Boosting (dpeaa)DE-He213 Tanha, Jafar (orcid)0000-0002-0779-6027 aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 14(2023), 10 vom: 10. Mai, Seite 3393-3411 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:14 year:2023 number:10 day:10 month:05 pages:3393-3411 https://dx.doi.org/10.1007/s13042-023-01839-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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_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_2119 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_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 10 10 05 3393-3411 |
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10.1007/s13042-023-01839-0 doi (DE-627)SPR052831086 (SPR)s13042-023-01839-0-e DE-627 ger DE-627 rakwb eng Mostafaei, Sahar Hassanzadeh verfasserin aut OUBoost: boosting based over and under sampling technique for handling imbalanced 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 real-world datasets usually contain imbalanced data. Learning from datasets where the number of samples in one class (minority) is much smaller than in another class (majority) creates biased classifiers to the majority class. The overall prediction accuracy in imbalanced datasets is higher than 90%, while this accuracy is relatively lower for minority classes. In this paper, we first propose a new technique for under-sampling based on the Peak clustering method from the majority class on imbalanced datasets. We then propose a novel boosting-based algorithm for learning from imbalanced datasets, based on a combination of the proposed Peak under-sampling algorithm and over-sampling technique (SMOTE) in the boosting procedure, named OUBoost. In the proposed OUBoost algorithm, misclassified examples are not given equal weights. OUBoost selects useful examples from the majority class and creates synthetic examples for the minority class. In fact, it indirectly updates the weights of samples. We designed experiments using several evaluation metrics, such as Recall, MCC, Gmean, and F-score on 30 real-world imbalanced datasets. The results show improved prediction performance in the minority class in most used datasets using OUBoost. We further report time comparisons and statistical tests to analyze our proposed algorithm in more details. Imbalanced data classification (dpeaa)DE-He213 Class imbalanced problem (dpeaa)DE-He213 Over-sampling (dpeaa)DE-He213 Under-sampling (dpeaa)DE-He213 Imbalance ratio (dpeaa)DE-He213 Boosting (dpeaa)DE-He213 Tanha, Jafar (orcid)0000-0002-0779-6027 aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 14(2023), 10 vom: 10. Mai, Seite 3393-3411 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:14 year:2023 number:10 day:10 month:05 pages:3393-3411 https://dx.doi.org/10.1007/s13042-023-01839-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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_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_2119 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_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 10 10 05 3393-3411 |
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10.1007/s13042-023-01839-0 doi (DE-627)SPR052831086 (SPR)s13042-023-01839-0-e DE-627 ger DE-627 rakwb eng Mostafaei, Sahar Hassanzadeh verfasserin aut OUBoost: boosting based over and under sampling technique for handling imbalanced 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 real-world datasets usually contain imbalanced data. Learning from datasets where the number of samples in one class (minority) is much smaller than in another class (majority) creates biased classifiers to the majority class. The overall prediction accuracy in imbalanced datasets is higher than 90%, while this accuracy is relatively lower for minority classes. In this paper, we first propose a new technique for under-sampling based on the Peak clustering method from the majority class on imbalanced datasets. We then propose a novel boosting-based algorithm for learning from imbalanced datasets, based on a combination of the proposed Peak under-sampling algorithm and over-sampling technique (SMOTE) in the boosting procedure, named OUBoost. In the proposed OUBoost algorithm, misclassified examples are not given equal weights. OUBoost selects useful examples from the majority class and creates synthetic examples for the minority class. In fact, it indirectly updates the weights of samples. We designed experiments using several evaluation metrics, such as Recall, MCC, Gmean, and F-score on 30 real-world imbalanced datasets. The results show improved prediction performance in the minority class in most used datasets using OUBoost. We further report time comparisons and statistical tests to analyze our proposed algorithm in more details. Imbalanced data classification (dpeaa)DE-He213 Class imbalanced problem (dpeaa)DE-He213 Over-sampling (dpeaa)DE-He213 Under-sampling (dpeaa)DE-He213 Imbalance ratio (dpeaa)DE-He213 Boosting (dpeaa)DE-He213 Tanha, Jafar (orcid)0000-0002-0779-6027 aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 14(2023), 10 vom: 10. Mai, Seite 3393-3411 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:14 year:2023 number:10 day:10 month:05 pages:3393-3411 https://dx.doi.org/10.1007/s13042-023-01839-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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_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_2119 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_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 10 10 05 3393-3411 |
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10.1007/s13042-023-01839-0 doi (DE-627)SPR052831086 (SPR)s13042-023-01839-0-e DE-627 ger DE-627 rakwb eng Mostafaei, Sahar Hassanzadeh verfasserin aut OUBoost: boosting based over and under sampling technique for handling imbalanced 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 real-world datasets usually contain imbalanced data. Learning from datasets where the number of samples in one class (minority) is much smaller than in another class (majority) creates biased classifiers to the majority class. The overall prediction accuracy in imbalanced datasets is higher than 90%, while this accuracy is relatively lower for minority classes. In this paper, we first propose a new technique for under-sampling based on the Peak clustering method from the majority class on imbalanced datasets. We then propose a novel boosting-based algorithm for learning from imbalanced datasets, based on a combination of the proposed Peak under-sampling algorithm and over-sampling technique (SMOTE) in the boosting procedure, named OUBoost. In the proposed OUBoost algorithm, misclassified examples are not given equal weights. OUBoost selects useful examples from the majority class and creates synthetic examples for the minority class. In fact, it indirectly updates the weights of samples. We designed experiments using several evaluation metrics, such as Recall, MCC, Gmean, and F-score on 30 real-world imbalanced datasets. The results show improved prediction performance in the minority class in most used datasets using OUBoost. We further report time comparisons and statistical tests to analyze our proposed algorithm in more details. Imbalanced data classification (dpeaa)DE-He213 Class imbalanced problem (dpeaa)DE-He213 Over-sampling (dpeaa)DE-He213 Under-sampling (dpeaa)DE-He213 Imbalance ratio (dpeaa)DE-He213 Boosting (dpeaa)DE-He213 Tanha, Jafar (orcid)0000-0002-0779-6027 aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 14(2023), 10 vom: 10. Mai, Seite 3393-3411 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:14 year:2023 number:10 day:10 month:05 pages:3393-3411 https://dx.doi.org/10.1007/s13042-023-01839-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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_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_2119 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_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 10 10 05 3393-3411 |
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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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Most real-world datasets usually contain imbalanced data. Learning from datasets where the number of samples in one class (minority) is much smaller than in another class (majority) creates biased classifiers to the majority class. The overall prediction accuracy in imbalanced datasets is higher than 90%, while this accuracy is relatively lower for minority classes. In this paper, we first propose a new technique for under-sampling based on the Peak clustering method from the majority class on imbalanced datasets. We then propose a novel boosting-based algorithm for learning from imbalanced datasets, based on a combination of the proposed Peak under-sampling algorithm and over-sampling technique (SMOTE) in the boosting procedure, named OUBoost. In the proposed OUBoost algorithm, misclassified examples are not given equal weights. OUBoost selects useful examples from the majority class and creates synthetic examples for the minority class. In fact, it indirectly updates the weights of samples. We designed experiments using several evaluation metrics, such as Recall, MCC, Gmean, and F-score on 30 real-world imbalanced datasets. The results show improved prediction performance in the minority class in most used datasets using OUBoost. We further report time comparisons and statistical tests to analyze our proposed algorithm in more details.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Imbalanced data classification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Class imbalanced problem</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Over-sampling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Under-sampling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Imbalance ratio</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Boosting</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tanha, Jafar</subfield><subfield code="0">(orcid)0000-0002-0779-6027</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of machine learning and cybernetics</subfield><subfield code="d">Heidelberg : Springer, 2010</subfield><subfield code="g">14(2023), 10 vom: 10. 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Mostafaei, Sahar Hassanzadeh |
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Mostafaei, Sahar Hassanzadeh misc Imbalanced data classification misc Class imbalanced problem misc Over-sampling misc Under-sampling misc Imbalance ratio misc Boosting OUBoost: boosting based over and under sampling technique for handling imbalanced data |
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OUBoost: boosting based over and under sampling technique for handling imbalanced data Imbalanced data classification (dpeaa)DE-He213 Class imbalanced problem (dpeaa)DE-He213 Over-sampling (dpeaa)DE-He213 Under-sampling (dpeaa)DE-He213 Imbalance ratio (dpeaa)DE-He213 Boosting (dpeaa)DE-He213 |
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misc Imbalanced data classification misc Class imbalanced problem misc Over-sampling misc Under-sampling misc Imbalance ratio misc Boosting |
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misc Imbalanced data classification misc Class imbalanced problem misc Over-sampling misc Under-sampling misc Imbalance ratio misc Boosting |
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OUBoost: boosting based over and under sampling technique for handling imbalanced data |
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ouboost: boosting based over and under sampling technique for handling imbalanced data |
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OUBoost: boosting based over and under sampling technique for handling imbalanced data |
| abstract |
Abstract Most real-world datasets usually contain imbalanced data. Learning from datasets where the number of samples in one class (minority) is much smaller than in another class (majority) creates biased classifiers to the majority class. The overall prediction accuracy in imbalanced datasets is higher than 90%, while this accuracy is relatively lower for minority classes. In this paper, we first propose a new technique for under-sampling based on the Peak clustering method from the majority class on imbalanced datasets. We then propose a novel boosting-based algorithm for learning from imbalanced datasets, based on a combination of the proposed Peak under-sampling algorithm and over-sampling technique (SMOTE) in the boosting procedure, named OUBoost. In the proposed OUBoost algorithm, misclassified examples are not given equal weights. OUBoost selects useful examples from the majority class and creates synthetic examples for the minority class. In fact, it indirectly updates the weights of samples. We designed experiments using several evaluation metrics, such as Recall, MCC, Gmean, and F-score on 30 real-world imbalanced datasets. The results show improved prediction performance in the minority class in most used datasets using OUBoost. We further report time comparisons and statistical tests to analyze our proposed algorithm in more details. © 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. |
| abstractGer |
Abstract Most real-world datasets usually contain imbalanced data. Learning from datasets where the number of samples in one class (minority) is much smaller than in another class (majority) creates biased classifiers to the majority class. The overall prediction accuracy in imbalanced datasets is higher than 90%, while this accuracy is relatively lower for minority classes. In this paper, we first propose a new technique for under-sampling based on the Peak clustering method from the majority class on imbalanced datasets. We then propose a novel boosting-based algorithm for learning from imbalanced datasets, based on a combination of the proposed Peak under-sampling algorithm and over-sampling technique (SMOTE) in the boosting procedure, named OUBoost. In the proposed OUBoost algorithm, misclassified examples are not given equal weights. OUBoost selects useful examples from the majority class and creates synthetic examples for the minority class. In fact, it indirectly updates the weights of samples. We designed experiments using several evaluation metrics, such as Recall, MCC, Gmean, and F-score on 30 real-world imbalanced datasets. The results show improved prediction performance in the minority class in most used datasets using OUBoost. We further report time comparisons and statistical tests to analyze our proposed algorithm in more details. © 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 real-world datasets usually contain imbalanced data. Learning from datasets where the number of samples in one class (minority) is much smaller than in another class (majority) creates biased classifiers to the majority class. The overall prediction accuracy in imbalanced datasets is higher than 90%, while this accuracy is relatively lower for minority classes. In this paper, we first propose a new technique for under-sampling based on the Peak clustering method from the majority class on imbalanced datasets. We then propose a novel boosting-based algorithm for learning from imbalanced datasets, based on a combination of the proposed Peak under-sampling algorithm and over-sampling technique (SMOTE) in the boosting procedure, named OUBoost. In the proposed OUBoost algorithm, misclassified examples are not given equal weights. OUBoost selects useful examples from the majority class and creates synthetic examples for the minority class. In fact, it indirectly updates the weights of samples. We designed experiments using several evaluation metrics, such as Recall, MCC, Gmean, and F-score on 30 real-world imbalanced datasets. The results show improved prediction performance in the minority class in most used datasets using OUBoost. We further report time comparisons and statistical tests to analyze our proposed algorithm in more details. © 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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| container_issue |
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| title_short |
OUBoost: boosting based over and under sampling technique for handling imbalanced data |
| url |
https://dx.doi.org/10.1007/s13042-023-01839-0 |
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| author2 |
Tanha, Jafar |
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| doi_str |
10.1007/s13042-023-01839-0 |
| up_date |
2024-07-03T15:05:37.409Z |
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| score |
7.403097 |