KGA: integrating KPCA and GAN for microbial data augmentation
Abstract The data used for microbial-based disease diagnosis are characterized by small sample sizes, imbalanced categories, high dimensionality, and strong sparsity. They pose challenges to machine learning algorithms that aim to achieve good classification performance. In this paper, we propose a...
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| Autor*in: |
Wen, Liu-Ying [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. 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(2022), 4 vom: 06. Nov., Seite 1427-1444 |
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| Übergeordnetes Werk: |
volume:14 ; year:2022 ; number:4 ; day:06 ; month:11 ; pages:1427-1444 |
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| DOI / URN: |
10.1007/s13042-022-01707-3 |
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| Katalog-ID: |
SPR049736744 |
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| 520 | |a Abstract The data used for microbial-based disease diagnosis are characterized by small sample sizes, imbalanced categories, high dimensionality, and strong sparsity. They pose challenges to machine learning algorithms that aim to achieve good classification performance. In this paper, we propose a two-stage data augmentation method to enhance training data quality. The first stage is feature transformation. We design a KPCA-based method to map microbial data to a low-rank feature space, resulting in cleaner and more efficient data representation. This processing step addresses high dimensionality and strong sparsity in microbial data. The second stage is data augmentation. New synthetic data are obtained by augmenting the positive samples through the GAN. The misclassification cost is used to control the ratio of positive/negative samples in new data. The combination of the augmented data with the original data constitutes a cost-sensitive dataset, which can increase sample diversity while addressing the imbalance problem. This is more reasonable than traditional sampling methods that resolve the class imbalance. We compare the new method with four popular data augmentation algorithms on 12 imbalanced datasets. The experimental results demonstrate that (1) the samples augmented by the proposed algorithm are more diverse than those generated using compared resampling methods, such as SMOTE_ENN, and (2) the proposed algorithm not only achieves the lowest total misclassification cost but also outperforms other methods in terms of %$F_2%$ and G-mean metrics. | ||
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| 650 | 4 | |a Data augmentation |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a KPCA |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Imbalanced data |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Zhang, Xiao-Min |4 aut | |
| 700 | 1 | |a Li, Qing-Feng |4 aut | |
| 700 | 1 | |a Min, Fan |0 (orcid)0000-0002-3290-1036 |4 aut | |
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10.1007/s13042-022-01707-3 doi (DE-627)SPR049736744 (SPR)s13042-022-01707-3-e DE-627 ger DE-627 rakwb eng Wen, Liu-Ying verfasserin aut KGA: integrating KPCA and GAN for microbial data augmentation 2022 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 2022. 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 The data used for microbial-based disease diagnosis are characterized by small sample sizes, imbalanced categories, high dimensionality, and strong sparsity. They pose challenges to machine learning algorithms that aim to achieve good classification performance. In this paper, we propose a two-stage data augmentation method to enhance training data quality. The first stage is feature transformation. We design a KPCA-based method to map microbial data to a low-rank feature space, resulting in cleaner and more efficient data representation. This processing step addresses high dimensionality and strong sparsity in microbial data. The second stage is data augmentation. New synthetic data are obtained by augmenting the positive samples through the GAN. The misclassification cost is used to control the ratio of positive/negative samples in new data. The combination of the augmented data with the original data constitutes a cost-sensitive dataset, which can increase sample diversity while addressing the imbalance problem. This is more reasonable than traditional sampling methods that resolve the class imbalance. We compare the new method with four popular data augmentation algorithms on 12 imbalanced datasets. The experimental results demonstrate that (1) the samples augmented by the proposed algorithm are more diverse than those generated using compared resampling methods, such as SMOTE_ENN, and (2) the proposed algorithm not only achieves the lowest total misclassification cost but also outperforms other methods in terms of %$F_2%$ and G-mean metrics. Cost-sensitive (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 GAN (dpeaa)DE-He213 KPCA (dpeaa)DE-He213 Imbalanced data (dpeaa)DE-He213 Zhang, Xiao-Min aut Li, Qing-Feng aut Min, Fan (orcid)0000-0002-3290-1036 aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 14(2022), 4 vom: 06. Nov., Seite 1427-1444 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:14 year:2022 number:4 day:06 month:11 pages:1427-1444 https://dx.doi.org/10.1007/s13042-022-01707-3 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 2022 4 06 11 1427-1444 |
| spelling |
10.1007/s13042-022-01707-3 doi (DE-627)SPR049736744 (SPR)s13042-022-01707-3-e DE-627 ger DE-627 rakwb eng Wen, Liu-Ying verfasserin aut KGA: integrating KPCA and GAN for microbial data augmentation 2022 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 2022. 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 The data used for microbial-based disease diagnosis are characterized by small sample sizes, imbalanced categories, high dimensionality, and strong sparsity. They pose challenges to machine learning algorithms that aim to achieve good classification performance. In this paper, we propose a two-stage data augmentation method to enhance training data quality. The first stage is feature transformation. We design a KPCA-based method to map microbial data to a low-rank feature space, resulting in cleaner and more efficient data representation. This processing step addresses high dimensionality and strong sparsity in microbial data. The second stage is data augmentation. New synthetic data are obtained by augmenting the positive samples through the GAN. The misclassification cost is used to control the ratio of positive/negative samples in new data. The combination of the augmented data with the original data constitutes a cost-sensitive dataset, which can increase sample diversity while addressing the imbalance problem. This is more reasonable than traditional sampling methods that resolve the class imbalance. We compare the new method with four popular data augmentation algorithms on 12 imbalanced datasets. The experimental results demonstrate that (1) the samples augmented by the proposed algorithm are more diverse than those generated using compared resampling methods, such as SMOTE_ENN, and (2) the proposed algorithm not only achieves the lowest total misclassification cost but also outperforms other methods in terms of %$F_2%$ and G-mean metrics. Cost-sensitive (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 GAN (dpeaa)DE-He213 KPCA (dpeaa)DE-He213 Imbalanced data (dpeaa)DE-He213 Zhang, Xiao-Min aut Li, Qing-Feng aut Min, Fan (orcid)0000-0002-3290-1036 aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 14(2022), 4 vom: 06. Nov., Seite 1427-1444 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:14 year:2022 number:4 day:06 month:11 pages:1427-1444 https://dx.doi.org/10.1007/s13042-022-01707-3 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 2022 4 06 11 1427-1444 |
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10.1007/s13042-022-01707-3 doi (DE-627)SPR049736744 (SPR)s13042-022-01707-3-e DE-627 ger DE-627 rakwb eng Wen, Liu-Ying verfasserin aut KGA: integrating KPCA and GAN for microbial data augmentation 2022 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 2022. 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 The data used for microbial-based disease diagnosis are characterized by small sample sizes, imbalanced categories, high dimensionality, and strong sparsity. They pose challenges to machine learning algorithms that aim to achieve good classification performance. In this paper, we propose a two-stage data augmentation method to enhance training data quality. The first stage is feature transformation. We design a KPCA-based method to map microbial data to a low-rank feature space, resulting in cleaner and more efficient data representation. This processing step addresses high dimensionality and strong sparsity in microbial data. The second stage is data augmentation. New synthetic data are obtained by augmenting the positive samples through the GAN. The misclassification cost is used to control the ratio of positive/negative samples in new data. The combination of the augmented data with the original data constitutes a cost-sensitive dataset, which can increase sample diversity while addressing the imbalance problem. This is more reasonable than traditional sampling methods that resolve the class imbalance. We compare the new method with four popular data augmentation algorithms on 12 imbalanced datasets. The experimental results demonstrate that (1) the samples augmented by the proposed algorithm are more diverse than those generated using compared resampling methods, such as SMOTE_ENN, and (2) the proposed algorithm not only achieves the lowest total misclassification cost but also outperforms other methods in terms of %$F_2%$ and G-mean metrics. Cost-sensitive (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 GAN (dpeaa)DE-He213 KPCA (dpeaa)DE-He213 Imbalanced data (dpeaa)DE-He213 Zhang, Xiao-Min aut Li, Qing-Feng aut Min, Fan (orcid)0000-0002-3290-1036 aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 14(2022), 4 vom: 06. Nov., Seite 1427-1444 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:14 year:2022 number:4 day:06 month:11 pages:1427-1444 https://dx.doi.org/10.1007/s13042-022-01707-3 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 2022 4 06 11 1427-1444 |
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10.1007/s13042-022-01707-3 doi (DE-627)SPR049736744 (SPR)s13042-022-01707-3-e DE-627 ger DE-627 rakwb eng Wen, Liu-Ying verfasserin aut KGA: integrating KPCA and GAN for microbial data augmentation 2022 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 2022. 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 The data used for microbial-based disease diagnosis are characterized by small sample sizes, imbalanced categories, high dimensionality, and strong sparsity. They pose challenges to machine learning algorithms that aim to achieve good classification performance. In this paper, we propose a two-stage data augmentation method to enhance training data quality. The first stage is feature transformation. We design a KPCA-based method to map microbial data to a low-rank feature space, resulting in cleaner and more efficient data representation. This processing step addresses high dimensionality and strong sparsity in microbial data. The second stage is data augmentation. New synthetic data are obtained by augmenting the positive samples through the GAN. The misclassification cost is used to control the ratio of positive/negative samples in new data. The combination of the augmented data with the original data constitutes a cost-sensitive dataset, which can increase sample diversity while addressing the imbalance problem. This is more reasonable than traditional sampling methods that resolve the class imbalance. We compare the new method with four popular data augmentation algorithms on 12 imbalanced datasets. The experimental results demonstrate that (1) the samples augmented by the proposed algorithm are more diverse than those generated using compared resampling methods, such as SMOTE_ENN, and (2) the proposed algorithm not only achieves the lowest total misclassification cost but also outperforms other methods in terms of %$F_2%$ and G-mean metrics. Cost-sensitive (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 GAN (dpeaa)DE-He213 KPCA (dpeaa)DE-He213 Imbalanced data (dpeaa)DE-He213 Zhang, Xiao-Min aut Li, Qing-Feng aut Min, Fan (orcid)0000-0002-3290-1036 aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 14(2022), 4 vom: 06. Nov., Seite 1427-1444 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:14 year:2022 number:4 day:06 month:11 pages:1427-1444 https://dx.doi.org/10.1007/s13042-022-01707-3 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 2022 4 06 11 1427-1444 |
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10.1007/s13042-022-01707-3 doi (DE-627)SPR049736744 (SPR)s13042-022-01707-3-e DE-627 ger DE-627 rakwb eng Wen, Liu-Ying verfasserin aut KGA: integrating KPCA and GAN for microbial data augmentation 2022 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 2022. 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 The data used for microbial-based disease diagnosis are characterized by small sample sizes, imbalanced categories, high dimensionality, and strong sparsity. They pose challenges to machine learning algorithms that aim to achieve good classification performance. In this paper, we propose a two-stage data augmentation method to enhance training data quality. The first stage is feature transformation. We design a KPCA-based method to map microbial data to a low-rank feature space, resulting in cleaner and more efficient data representation. This processing step addresses high dimensionality and strong sparsity in microbial data. The second stage is data augmentation. New synthetic data are obtained by augmenting the positive samples through the GAN. The misclassification cost is used to control the ratio of positive/negative samples in new data. The combination of the augmented data with the original data constitutes a cost-sensitive dataset, which can increase sample diversity while addressing the imbalance problem. This is more reasonable than traditional sampling methods that resolve the class imbalance. We compare the new method with four popular data augmentation algorithms on 12 imbalanced datasets. The experimental results demonstrate that (1) the samples augmented by the proposed algorithm are more diverse than those generated using compared resampling methods, such as SMOTE_ENN, and (2) the proposed algorithm not only achieves the lowest total misclassification cost but also outperforms other methods in terms of %$F_2%$ and G-mean metrics. Cost-sensitive (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 GAN (dpeaa)DE-He213 KPCA (dpeaa)DE-He213 Imbalanced data (dpeaa)DE-He213 Zhang, Xiao-Min aut Li, Qing-Feng aut Min, Fan (orcid)0000-0002-3290-1036 aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 14(2022), 4 vom: 06. Nov., Seite 1427-1444 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:14 year:2022 number:4 day:06 month:11 pages:1427-1444 https://dx.doi.org/10.1007/s13042-022-01707-3 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 2022 4 06 11 1427-1444 |
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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 The data used for microbial-based disease diagnosis are characterized by small sample sizes, imbalanced categories, high dimensionality, and strong sparsity. They pose challenges to machine learning algorithms that aim to achieve good classification performance. In this paper, we propose a two-stage data augmentation method to enhance training data quality. The first stage is feature transformation. We design a KPCA-based method to map microbial data to a low-rank feature space, resulting in cleaner and more efficient data representation. This processing step addresses high dimensionality and strong sparsity in microbial data. The second stage is data augmentation. New synthetic data are obtained by augmenting the positive samples through the GAN. The misclassification cost is used to control the ratio of positive/negative samples in new data. The combination of the augmented data with the original data constitutes a cost-sensitive dataset, which can increase sample diversity while addressing the imbalance problem. This is more reasonable than traditional sampling methods that resolve the class imbalance. We compare the new method with four popular data augmentation algorithms on 12 imbalanced datasets. The experimental results demonstrate that (1) the samples augmented by the proposed algorithm are more diverse than those generated using compared resampling methods, such as SMOTE_ENN, and (2) the proposed algorithm not only achieves the lowest total misclassification cost but also outperforms other methods in terms of %$F_2%$ and G-mean metrics.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cost-sensitive</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data augmentation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">GAN</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">KPCA</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Imbalanced data</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Xiao-Min</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Qing-Feng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Min, Fan</subfield><subfield code="0">(orcid)0000-0002-3290-1036</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(2022), 4 vom: 06. 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kga: integrating kpca and gan for microbial data augmentation |
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KGA: integrating KPCA and GAN for microbial data augmentation |
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Abstract The data used for microbial-based disease diagnosis are characterized by small sample sizes, imbalanced categories, high dimensionality, and strong sparsity. They pose challenges to machine learning algorithms that aim to achieve good classification performance. In this paper, we propose a two-stage data augmentation method to enhance training data quality. The first stage is feature transformation. We design a KPCA-based method to map microbial data to a low-rank feature space, resulting in cleaner and more efficient data representation. This processing step addresses high dimensionality and strong sparsity in microbial data. The second stage is data augmentation. New synthetic data are obtained by augmenting the positive samples through the GAN. The misclassification cost is used to control the ratio of positive/negative samples in new data. The combination of the augmented data with the original data constitutes a cost-sensitive dataset, which can increase sample diversity while addressing the imbalance problem. This is more reasonable than traditional sampling methods that resolve the class imbalance. We compare the new method with four popular data augmentation algorithms on 12 imbalanced datasets. The experimental results demonstrate that (1) the samples augmented by the proposed algorithm are more diverse than those generated using compared resampling methods, such as SMOTE_ENN, and (2) the proposed algorithm not only achieves the lowest total misclassification cost but also outperforms other methods in terms of %$F_2%$ and G-mean metrics. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract The data used for microbial-based disease diagnosis are characterized by small sample sizes, imbalanced categories, high dimensionality, and strong sparsity. They pose challenges to machine learning algorithms that aim to achieve good classification performance. In this paper, we propose a two-stage data augmentation method to enhance training data quality. The first stage is feature transformation. We design a KPCA-based method to map microbial data to a low-rank feature space, resulting in cleaner and more efficient data representation. This processing step addresses high dimensionality and strong sparsity in microbial data. The second stage is data augmentation. New synthetic data are obtained by augmenting the positive samples through the GAN. The misclassification cost is used to control the ratio of positive/negative samples in new data. The combination of the augmented data with the original data constitutes a cost-sensitive dataset, which can increase sample diversity while addressing the imbalance problem. This is more reasonable than traditional sampling methods that resolve the class imbalance. We compare the new method with four popular data augmentation algorithms on 12 imbalanced datasets. The experimental results demonstrate that (1) the samples augmented by the proposed algorithm are more diverse than those generated using compared resampling methods, such as SMOTE_ENN, and (2) the proposed algorithm not only achieves the lowest total misclassification cost but also outperforms other methods in terms of %$F_2%$ and G-mean metrics. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. 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 The data used for microbial-based disease diagnosis are characterized by small sample sizes, imbalanced categories, high dimensionality, and strong sparsity. They pose challenges to machine learning algorithms that aim to achieve good classification performance. In this paper, we propose a two-stage data augmentation method to enhance training data quality. The first stage is feature transformation. We design a KPCA-based method to map microbial data to a low-rank feature space, resulting in cleaner and more efficient data representation. This processing step addresses high dimensionality and strong sparsity in microbial data. The second stage is data augmentation. New synthetic data are obtained by augmenting the positive samples through the GAN. The misclassification cost is used to control the ratio of positive/negative samples in new data. The combination of the augmented data with the original data constitutes a cost-sensitive dataset, which can increase sample diversity while addressing the imbalance problem. This is more reasonable than traditional sampling methods that resolve the class imbalance. We compare the new method with four popular data augmentation algorithms on 12 imbalanced datasets. The experimental results demonstrate that (1) the samples augmented by the proposed algorithm are more diverse than those generated using compared resampling methods, such as SMOTE_ENN, and (2) the proposed algorithm not only achieves the lowest total misclassification cost but also outperforms other methods in terms of %$F_2%$ and G-mean metrics. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. 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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| title_short |
KGA: integrating KPCA and GAN for microbial data augmentation |
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https://dx.doi.org/10.1007/s13042-022-01707-3 |
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true |
| author2 |
Zhang, Xiao-Min Li, Qing-Feng Min, Fan |
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Zhang, Xiao-Min Li, Qing-Feng Min, Fan |
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10.1007/s13042-022-01707-3 |
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2024-07-04T02:05:12.953Z |
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| score |
7.3983927 |