Optimized Bayesian adaptive resonance theory mapping model using a rational quadratic kernel and Bayesian quadratic regularization
Abstract Bayesian adaptive resonance theory (ART) and ARTMAP-based neural network classifier (known as BAM) are widely used and achieve good classification performance when solving the problem of the undefinable number of clusters and diffusion of classes found in other networks based on ART, such a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yang, Shunkun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 52(2021), 7 vom: 11. Okt., Seite 7777-7792 |
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| Übergeordnetes Werk: |
volume:52 ; year:2021 ; number:7 ; day:11 ; month:10 ; pages:7777-7792 |
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| DOI / URN: |
10.1007/s10489-021-02883-5 |
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| Katalog-ID: |
SPR046914773 |
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| 520 | |a Abstract Bayesian adaptive resonance theory (ART) and ARTMAP-based neural network classifier (known as BAM) are widely used and achieve good classification performance when solving the problem of the undefinable number of clusters and diffusion of classes found in other networks based on ART, such as fuzzy ART. However, the existing BAM classification model is not sufficiently stable to calculate the likelihood when dealing with a small number of highdimensional data. Further, it is difficult to achieve global convergence, which can affect classification performance. To solve these issues and improve the generalization ability of the BAM approach, we propose a BAM classification model that incorporates two overfitting suppression mechanisms. The first mechanism is based on a rational quadratic kernel function to reduces the sensitivity at the cluster decision boundary to novel data, which improves the adaptability of the model to small samples. The second mechanism is based on Bayesian quadratic regularization and reduces the dependence of the classifier on the likelihood estimation and the prior probability, thus preventing overfitting. Experimental results on six different datasets show that the proposed model improves the accuracy, precision, recall, and F1-score of BAM by 8.65%, 11.17%, 30.89%, and 19.25%, respectively. | ||
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| 700 | 1 | |a Bian, Chong |4 aut | |
| 700 | 1 | |a Shao, Qi |4 aut | |
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10.1007/s10489-021-02883-5 doi (DE-627)SPR046914773 (SPR)s10489-021-02883-5-e DE-627 ger DE-627 rakwb eng Yang, Shunkun verfasserin aut Optimized Bayesian adaptive resonance theory mapping model using a rational quadratic kernel and Bayesian quadratic regularization 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Bayesian adaptive resonance theory (ART) and ARTMAP-based neural network classifier (known as BAM) are widely used and achieve good classification performance when solving the problem of the undefinable number of clusters and diffusion of classes found in other networks based on ART, such as fuzzy ART. However, the existing BAM classification model is not sufficiently stable to calculate the likelihood when dealing with a small number of highdimensional data. Further, it is difficult to achieve global convergence, which can affect classification performance. To solve these issues and improve the generalization ability of the BAM approach, we propose a BAM classification model that incorporates two overfitting suppression mechanisms. The first mechanism is based on a rational quadratic kernel function to reduces the sensitivity at the cluster decision boundary to novel data, which improves the adaptability of the model to small samples. The second mechanism is based on Bayesian quadratic regularization and reduces the dependence of the classifier on the likelihood estimation and the prior probability, thus preventing overfitting. Experimental results on six different datasets show that the proposed model improves the accuracy, precision, recall, and F1-score of BAM by 8.65%, 11.17%, 30.89%, and 19.25%, respectively. Bayesian adaptive resonance theory mapping model (dpeaa)DE-He213 Small sample (dpeaa)DE-He213 Rational quadratic kernel (dpeaa)DE-He213 Bayesian quadratic regularization (dpeaa)DE-He213 Li, Hongman aut Gou, Xiaodong (orcid)0000-0002-5200-6143 aut Bian, Chong aut Shao, Qi aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2021), 7 vom: 11. Okt., Seite 7777-7792 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2021 number:7 day:11 month:10 pages:7777-7792 https://dx.doi.org/10.1007/s10489-021-02883-5 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_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_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 52 2021 7 11 10 7777-7792 |
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10.1007/s10489-021-02883-5 doi (DE-627)SPR046914773 (SPR)s10489-021-02883-5-e DE-627 ger DE-627 rakwb eng Yang, Shunkun verfasserin aut Optimized Bayesian adaptive resonance theory mapping model using a rational quadratic kernel and Bayesian quadratic regularization 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Bayesian adaptive resonance theory (ART) and ARTMAP-based neural network classifier (known as BAM) are widely used and achieve good classification performance when solving the problem of the undefinable number of clusters and diffusion of classes found in other networks based on ART, such as fuzzy ART. However, the existing BAM classification model is not sufficiently stable to calculate the likelihood when dealing with a small number of highdimensional data. Further, it is difficult to achieve global convergence, which can affect classification performance. To solve these issues and improve the generalization ability of the BAM approach, we propose a BAM classification model that incorporates two overfitting suppression mechanisms. The first mechanism is based on a rational quadratic kernel function to reduces the sensitivity at the cluster decision boundary to novel data, which improves the adaptability of the model to small samples. The second mechanism is based on Bayesian quadratic regularization and reduces the dependence of the classifier on the likelihood estimation and the prior probability, thus preventing overfitting. Experimental results on six different datasets show that the proposed model improves the accuracy, precision, recall, and F1-score of BAM by 8.65%, 11.17%, 30.89%, and 19.25%, respectively. Bayesian adaptive resonance theory mapping model (dpeaa)DE-He213 Small sample (dpeaa)DE-He213 Rational quadratic kernel (dpeaa)DE-He213 Bayesian quadratic regularization (dpeaa)DE-He213 Li, Hongman aut Gou, Xiaodong (orcid)0000-0002-5200-6143 aut Bian, Chong aut Shao, Qi aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2021), 7 vom: 11. Okt., Seite 7777-7792 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2021 number:7 day:11 month:10 pages:7777-7792 https://dx.doi.org/10.1007/s10489-021-02883-5 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_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_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 52 2021 7 11 10 7777-7792 |
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10.1007/s10489-021-02883-5 doi (DE-627)SPR046914773 (SPR)s10489-021-02883-5-e DE-627 ger DE-627 rakwb eng Yang, Shunkun verfasserin aut Optimized Bayesian adaptive resonance theory mapping model using a rational quadratic kernel and Bayesian quadratic regularization 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Bayesian adaptive resonance theory (ART) and ARTMAP-based neural network classifier (known as BAM) are widely used and achieve good classification performance when solving the problem of the undefinable number of clusters and diffusion of classes found in other networks based on ART, such as fuzzy ART. However, the existing BAM classification model is not sufficiently stable to calculate the likelihood when dealing with a small number of highdimensional data. Further, it is difficult to achieve global convergence, which can affect classification performance. To solve these issues and improve the generalization ability of the BAM approach, we propose a BAM classification model that incorporates two overfitting suppression mechanisms. The first mechanism is based on a rational quadratic kernel function to reduces the sensitivity at the cluster decision boundary to novel data, which improves the adaptability of the model to small samples. The second mechanism is based on Bayesian quadratic regularization and reduces the dependence of the classifier on the likelihood estimation and the prior probability, thus preventing overfitting. Experimental results on six different datasets show that the proposed model improves the accuracy, precision, recall, and F1-score of BAM by 8.65%, 11.17%, 30.89%, and 19.25%, respectively. Bayesian adaptive resonance theory mapping model (dpeaa)DE-He213 Small sample (dpeaa)DE-He213 Rational quadratic kernel (dpeaa)DE-He213 Bayesian quadratic regularization (dpeaa)DE-He213 Li, Hongman aut Gou, Xiaodong (orcid)0000-0002-5200-6143 aut Bian, Chong aut Shao, Qi aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2021), 7 vom: 11. Okt., Seite 7777-7792 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2021 number:7 day:11 month:10 pages:7777-7792 https://dx.doi.org/10.1007/s10489-021-02883-5 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_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_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 52 2021 7 11 10 7777-7792 |
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10.1007/s10489-021-02883-5 doi (DE-627)SPR046914773 (SPR)s10489-021-02883-5-e DE-627 ger DE-627 rakwb eng Yang, Shunkun verfasserin aut Optimized Bayesian adaptive resonance theory mapping model using a rational quadratic kernel and Bayesian quadratic regularization 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Bayesian adaptive resonance theory (ART) and ARTMAP-based neural network classifier (known as BAM) are widely used and achieve good classification performance when solving the problem of the undefinable number of clusters and diffusion of classes found in other networks based on ART, such as fuzzy ART. However, the existing BAM classification model is not sufficiently stable to calculate the likelihood when dealing with a small number of highdimensional data. Further, it is difficult to achieve global convergence, which can affect classification performance. To solve these issues and improve the generalization ability of the BAM approach, we propose a BAM classification model that incorporates two overfitting suppression mechanisms. The first mechanism is based on a rational quadratic kernel function to reduces the sensitivity at the cluster decision boundary to novel data, which improves the adaptability of the model to small samples. The second mechanism is based on Bayesian quadratic regularization and reduces the dependence of the classifier on the likelihood estimation and the prior probability, thus preventing overfitting. Experimental results on six different datasets show that the proposed model improves the accuracy, precision, recall, and F1-score of BAM by 8.65%, 11.17%, 30.89%, and 19.25%, respectively. Bayesian adaptive resonance theory mapping model (dpeaa)DE-He213 Small sample (dpeaa)DE-He213 Rational quadratic kernel (dpeaa)DE-He213 Bayesian quadratic regularization (dpeaa)DE-He213 Li, Hongman aut Gou, Xiaodong (orcid)0000-0002-5200-6143 aut Bian, Chong aut Shao, Qi aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2021), 7 vom: 11. Okt., Seite 7777-7792 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2021 number:7 day:11 month:10 pages:7777-7792 https://dx.doi.org/10.1007/s10489-021-02883-5 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_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_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 52 2021 7 11 10 7777-7792 |
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10.1007/s10489-021-02883-5 doi (DE-627)SPR046914773 (SPR)s10489-021-02883-5-e DE-627 ger DE-627 rakwb eng Yang, Shunkun verfasserin aut Optimized Bayesian adaptive resonance theory mapping model using a rational quadratic kernel and Bayesian quadratic regularization 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Bayesian adaptive resonance theory (ART) and ARTMAP-based neural network classifier (known as BAM) are widely used and achieve good classification performance when solving the problem of the undefinable number of clusters and diffusion of classes found in other networks based on ART, such as fuzzy ART. However, the existing BAM classification model is not sufficiently stable to calculate the likelihood when dealing with a small number of highdimensional data. Further, it is difficult to achieve global convergence, which can affect classification performance. To solve these issues and improve the generalization ability of the BAM approach, we propose a BAM classification model that incorporates two overfitting suppression mechanisms. The first mechanism is based on a rational quadratic kernel function to reduces the sensitivity at the cluster decision boundary to novel data, which improves the adaptability of the model to small samples. The second mechanism is based on Bayesian quadratic regularization and reduces the dependence of the classifier on the likelihood estimation and the prior probability, thus preventing overfitting. Experimental results on six different datasets show that the proposed model improves the accuracy, precision, recall, and F1-score of BAM by 8.65%, 11.17%, 30.89%, and 19.25%, respectively. Bayesian adaptive resonance theory mapping model (dpeaa)DE-He213 Small sample (dpeaa)DE-He213 Rational quadratic kernel (dpeaa)DE-He213 Bayesian quadratic regularization (dpeaa)DE-He213 Li, Hongman aut Gou, Xiaodong (orcid)0000-0002-5200-6143 aut Bian, Chong aut Shao, Qi aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2021), 7 vom: 11. Okt., Seite 7777-7792 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2021 number:7 day:11 month:10 pages:7777-7792 https://dx.doi.org/10.1007/s10489-021-02883-5 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_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_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 52 2021 7 11 10 7777-7792 |
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However, the existing BAM classification model is not sufficiently stable to calculate the likelihood when dealing with a small number of highdimensional data. Further, it is difficult to achieve global convergence, which can affect classification performance. To solve these issues and improve the generalization ability of the BAM approach, we propose a BAM classification model that incorporates two overfitting suppression mechanisms. The first mechanism is based on a rational quadratic kernel function to reduces the sensitivity at the cluster decision boundary to novel data, which improves the adaptability of the model to small samples. The second mechanism is based on Bayesian quadratic regularization and reduces the dependence of the classifier on the likelihood estimation and the prior probability, thus preventing overfitting. 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Optimized Bayesian adaptive resonance theory mapping model using a rational quadratic kernel and Bayesian quadratic regularization |
| abstract |
Abstract Bayesian adaptive resonance theory (ART) and ARTMAP-based neural network classifier (known as BAM) are widely used and achieve good classification performance when solving the problem of the undefinable number of clusters and diffusion of classes found in other networks based on ART, such as fuzzy ART. However, the existing BAM classification model is not sufficiently stable to calculate the likelihood when dealing with a small number of highdimensional data. Further, it is difficult to achieve global convergence, which can affect classification performance. To solve these issues and improve the generalization ability of the BAM approach, we propose a BAM classification model that incorporates two overfitting suppression mechanisms. The first mechanism is based on a rational quadratic kernel function to reduces the sensitivity at the cluster decision boundary to novel data, which improves the adaptability of the model to small samples. The second mechanism is based on Bayesian quadratic regularization and reduces the dependence of the classifier on the likelihood estimation and the prior probability, thus preventing overfitting. Experimental results on six different datasets show that the proposed model improves the accuracy, precision, recall, and F1-score of BAM by 8.65%, 11.17%, 30.89%, and 19.25%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract Bayesian adaptive resonance theory (ART) and ARTMAP-based neural network classifier (known as BAM) are widely used and achieve good classification performance when solving the problem of the undefinable number of clusters and diffusion of classes found in other networks based on ART, such as fuzzy ART. However, the existing BAM classification model is not sufficiently stable to calculate the likelihood when dealing with a small number of highdimensional data. Further, it is difficult to achieve global convergence, which can affect classification performance. To solve these issues and improve the generalization ability of the BAM approach, we propose a BAM classification model that incorporates two overfitting suppression mechanisms. The first mechanism is based on a rational quadratic kernel function to reduces the sensitivity at the cluster decision boundary to novel data, which improves the adaptability of the model to small samples. The second mechanism is based on Bayesian quadratic regularization and reduces the dependence of the classifier on the likelihood estimation and the prior probability, thus preventing overfitting. Experimental results on six different datasets show that the proposed model improves the accuracy, precision, recall, and F1-score of BAM by 8.65%, 11.17%, 30.89%, and 19.25%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Bayesian adaptive resonance theory (ART) and ARTMAP-based neural network classifier (known as BAM) are widely used and achieve good classification performance when solving the problem of the undefinable number of clusters and diffusion of classes found in other networks based on ART, such as fuzzy ART. However, the existing BAM classification model is not sufficiently stable to calculate the likelihood when dealing with a small number of highdimensional data. Further, it is difficult to achieve global convergence, which can affect classification performance. To solve these issues and improve the generalization ability of the BAM approach, we propose a BAM classification model that incorporates two overfitting suppression mechanisms. The first mechanism is based on a rational quadratic kernel function to reduces the sensitivity at the cluster decision boundary to novel data, which improves the adaptability of the model to small samples. The second mechanism is based on Bayesian quadratic regularization and reduces the dependence of the classifier on the likelihood estimation and the prior probability, thus preventing overfitting. Experimental results on six different datasets show that the proposed model improves the accuracy, precision, recall, and F1-score of BAM by 8.65%, 11.17%, 30.89%, and 19.25%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| title_short |
Optimized Bayesian adaptive resonance theory mapping model using a rational quadratic kernel and Bayesian quadratic regularization |
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https://dx.doi.org/10.1007/s10489-021-02883-5 |
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Li, Hongman Gou, Xiaodong Bian, Chong Shao, Qi |
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Li, Hongman Gou, Xiaodong Bian, Chong Shao, Qi |
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| doi_str |
10.1007/s10489-021-02883-5 |
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2024-07-04T01:01:03.634Z |
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| score |
7.4006987 |