Dsa-PAML: a parallel automated machine learning system via dual-stacked autoencoder
Abstract Finding a high-performance machine learning pipeline (ML pipeline) for a supervised learning task takes much time. It requires many choices, including preprocessing datasets, selecting algorithms, tuning hyperparameters, and ensembling candidate models. With increasing pipelines arises a co...
Ausführliche Beschreibung
Autor*in: |
Liu, Pengjie [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2022 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
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Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - London : Springer, 1993, 34(2022), 15 vom: 28. März, Seite 12985-13006 |
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Übergeordnetes Werk: |
volume:34 ; year:2022 ; number:15 ; day:28 ; month:03 ; pages:12985-13006 |
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DOI / URN: |
10.1007/s00521-022-07119-2 |
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Katalog-ID: |
SPR047664711 |
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520 | |a Abstract Finding a high-performance machine learning pipeline (ML pipeline) for a supervised learning task takes much time. It requires many choices, including preprocessing datasets, selecting algorithms, tuning hyperparameters, and ensembling candidate models. With increasing pipelines arises a combination explosion problem. This work presents a new automated machine learning (AutoML) system called Dsa-PAML to address this challenge by recommending, training, and ensembling suitable models for supervised learning tasks. Dsa-PAML is a parallel automated system based on a dual-stacked autoencoder (Dsa). Firstly, meta-features of datasets and ML pipelines are used to alleviate cold-start recommendation problems. Secondly, a novel dual-stacked autoencoder is used to simultaneously learn the latent features of datasets and ML pipelines, efficiently learning collaborations of both datasets and ML pipelines and recommending suitable ML pipelines for a new dataset. Thirdly, Dsa-PAML can train the recommended ML pipelines on the new dataset in a parallel method, which substantially reduces the time complexity of the proposed method. Finally, a parallel selective ensemble system is embedded into Dsa-PAML. It selects base models from candidate ML pipelines according to their runtime, classification performance, and diversity on the validation set, enhancing Dsa-PAML’s stability for most datasets. Amounts of experiments on 30 UCI datasets show that our approach outperforms current state-of-the-art methods. | ||
650 | 4 | |a Automated machine learning |7 (dpeaa)DE-He213 | |
650 | 4 | |a Parallel automated system |7 (dpeaa)DE-He213 | |
650 | 4 | |a Dual-stacked autoencoder |7 (dpeaa)DE-He213 | |
650 | 4 | |a Selective ensemble |7 (dpeaa)DE-He213 | |
700 | 1 | |a Pan, Fucheng |4 aut | |
700 | 1 | |a Zhou, Xiaofeng |0 (orcid)0000-0001-9837-1261 |4 aut | |
700 | 1 | |a Li, Shuai |4 aut | |
700 | 1 | |a Zeng, Pengyu |4 aut | |
700 | 1 | |a Liu, Shurui |4 aut | |
700 | 1 | |a Jin, Liang |4 aut | |
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10.1007/s00521-022-07119-2 doi (DE-627)SPR047664711 (SPR)s00521-022-07119-2-e DE-627 ger DE-627 rakwb eng Liu, Pengjie verfasserin aut Dsa-PAML: a parallel automated machine learning system via dual-stacked autoencoder 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Finding a high-performance machine learning pipeline (ML pipeline) for a supervised learning task takes much time. It requires many choices, including preprocessing datasets, selecting algorithms, tuning hyperparameters, and ensembling candidate models. With increasing pipelines arises a combination explosion problem. This work presents a new automated machine learning (AutoML) system called Dsa-PAML to address this challenge by recommending, training, and ensembling suitable models for supervised learning tasks. Dsa-PAML is a parallel automated system based on a dual-stacked autoencoder (Dsa). Firstly, meta-features of datasets and ML pipelines are used to alleviate cold-start recommendation problems. Secondly, a novel dual-stacked autoencoder is used to simultaneously learn the latent features of datasets and ML pipelines, efficiently learning collaborations of both datasets and ML pipelines and recommending suitable ML pipelines for a new dataset. Thirdly, Dsa-PAML can train the recommended ML pipelines on the new dataset in a parallel method, which substantially reduces the time complexity of the proposed method. Finally, a parallel selective ensemble system is embedded into Dsa-PAML. It selects base models from candidate ML pipelines according to their runtime, classification performance, and diversity on the validation set, enhancing Dsa-PAML’s stability for most datasets. Amounts of experiments on 30 UCI datasets show that our approach outperforms current state-of-the-art methods. Automated machine learning (dpeaa)DE-He213 Parallel automated system (dpeaa)DE-He213 Dual-stacked autoencoder (dpeaa)DE-He213 Selective ensemble (dpeaa)DE-He213 Pan, Fucheng aut Zhou, Xiaofeng (orcid)0000-0001-9837-1261 aut Li, Shuai aut Zeng, Pengyu aut Liu, Shurui aut Jin, Liang aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 15 vom: 28. März, Seite 12985-13006 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:15 day:28 month:03 pages:12985-13006 https://dx.doi.org/10.1007/s00521-022-07119-2 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 34 2022 15 28 03 12985-13006 |
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10.1007/s00521-022-07119-2 doi (DE-627)SPR047664711 (SPR)s00521-022-07119-2-e DE-627 ger DE-627 rakwb eng Liu, Pengjie verfasserin aut Dsa-PAML: a parallel automated machine learning system via dual-stacked autoencoder 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Finding a high-performance machine learning pipeline (ML pipeline) for a supervised learning task takes much time. It requires many choices, including preprocessing datasets, selecting algorithms, tuning hyperparameters, and ensembling candidate models. With increasing pipelines arises a combination explosion problem. This work presents a new automated machine learning (AutoML) system called Dsa-PAML to address this challenge by recommending, training, and ensembling suitable models for supervised learning tasks. Dsa-PAML is a parallel automated system based on a dual-stacked autoencoder (Dsa). Firstly, meta-features of datasets and ML pipelines are used to alleviate cold-start recommendation problems. Secondly, a novel dual-stacked autoencoder is used to simultaneously learn the latent features of datasets and ML pipelines, efficiently learning collaborations of both datasets and ML pipelines and recommending suitable ML pipelines for a new dataset. Thirdly, Dsa-PAML can train the recommended ML pipelines on the new dataset in a parallel method, which substantially reduces the time complexity of the proposed method. Finally, a parallel selective ensemble system is embedded into Dsa-PAML. It selects base models from candidate ML pipelines according to their runtime, classification performance, and diversity on the validation set, enhancing Dsa-PAML’s stability for most datasets. Amounts of experiments on 30 UCI datasets show that our approach outperforms current state-of-the-art methods. Automated machine learning (dpeaa)DE-He213 Parallel automated system (dpeaa)DE-He213 Dual-stacked autoencoder (dpeaa)DE-He213 Selective ensemble (dpeaa)DE-He213 Pan, Fucheng aut Zhou, Xiaofeng (orcid)0000-0001-9837-1261 aut Li, Shuai aut Zeng, Pengyu aut Liu, Shurui aut Jin, Liang aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 15 vom: 28. März, Seite 12985-13006 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:15 day:28 month:03 pages:12985-13006 https://dx.doi.org/10.1007/s00521-022-07119-2 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 34 2022 15 28 03 12985-13006 |
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10.1007/s00521-022-07119-2 doi (DE-627)SPR047664711 (SPR)s00521-022-07119-2-e DE-627 ger DE-627 rakwb eng Liu, Pengjie verfasserin aut Dsa-PAML: a parallel automated machine learning system via dual-stacked autoencoder 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Finding a high-performance machine learning pipeline (ML pipeline) for a supervised learning task takes much time. It requires many choices, including preprocessing datasets, selecting algorithms, tuning hyperparameters, and ensembling candidate models. With increasing pipelines arises a combination explosion problem. This work presents a new automated machine learning (AutoML) system called Dsa-PAML to address this challenge by recommending, training, and ensembling suitable models for supervised learning tasks. Dsa-PAML is a parallel automated system based on a dual-stacked autoencoder (Dsa). Firstly, meta-features of datasets and ML pipelines are used to alleviate cold-start recommendation problems. Secondly, a novel dual-stacked autoencoder is used to simultaneously learn the latent features of datasets and ML pipelines, efficiently learning collaborations of both datasets and ML pipelines and recommending suitable ML pipelines for a new dataset. Thirdly, Dsa-PAML can train the recommended ML pipelines on the new dataset in a parallel method, which substantially reduces the time complexity of the proposed method. Finally, a parallel selective ensemble system is embedded into Dsa-PAML. It selects base models from candidate ML pipelines according to their runtime, classification performance, and diversity on the validation set, enhancing Dsa-PAML’s stability for most datasets. Amounts of experiments on 30 UCI datasets show that our approach outperforms current state-of-the-art methods. Automated machine learning (dpeaa)DE-He213 Parallel automated system (dpeaa)DE-He213 Dual-stacked autoencoder (dpeaa)DE-He213 Selective ensemble (dpeaa)DE-He213 Pan, Fucheng aut Zhou, Xiaofeng (orcid)0000-0001-9837-1261 aut Li, Shuai aut Zeng, Pengyu aut Liu, Shurui aut Jin, Liang aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 15 vom: 28. März, Seite 12985-13006 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:15 day:28 month:03 pages:12985-13006 https://dx.doi.org/10.1007/s00521-022-07119-2 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 34 2022 15 28 03 12985-13006 |
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10.1007/s00521-022-07119-2 doi (DE-627)SPR047664711 (SPR)s00521-022-07119-2-e DE-627 ger DE-627 rakwb eng Liu, Pengjie verfasserin aut Dsa-PAML: a parallel automated machine learning system via dual-stacked autoencoder 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Finding a high-performance machine learning pipeline (ML pipeline) for a supervised learning task takes much time. It requires many choices, including preprocessing datasets, selecting algorithms, tuning hyperparameters, and ensembling candidate models. With increasing pipelines arises a combination explosion problem. This work presents a new automated machine learning (AutoML) system called Dsa-PAML to address this challenge by recommending, training, and ensembling suitable models for supervised learning tasks. Dsa-PAML is a parallel automated system based on a dual-stacked autoencoder (Dsa). Firstly, meta-features of datasets and ML pipelines are used to alleviate cold-start recommendation problems. Secondly, a novel dual-stacked autoencoder is used to simultaneously learn the latent features of datasets and ML pipelines, efficiently learning collaborations of both datasets and ML pipelines and recommending suitable ML pipelines for a new dataset. Thirdly, Dsa-PAML can train the recommended ML pipelines on the new dataset in a parallel method, which substantially reduces the time complexity of the proposed method. Finally, a parallel selective ensemble system is embedded into Dsa-PAML. It selects base models from candidate ML pipelines according to their runtime, classification performance, and diversity on the validation set, enhancing Dsa-PAML’s stability for most datasets. Amounts of experiments on 30 UCI datasets show that our approach outperforms current state-of-the-art methods. Automated machine learning (dpeaa)DE-He213 Parallel automated system (dpeaa)DE-He213 Dual-stacked autoencoder (dpeaa)DE-He213 Selective ensemble (dpeaa)DE-He213 Pan, Fucheng aut Zhou, Xiaofeng (orcid)0000-0001-9837-1261 aut Li, Shuai aut Zeng, Pengyu aut Liu, Shurui aut Jin, Liang aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 15 vom: 28. März, Seite 12985-13006 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:15 day:28 month:03 pages:12985-13006 https://dx.doi.org/10.1007/s00521-022-07119-2 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 34 2022 15 28 03 12985-13006 |
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10.1007/s00521-022-07119-2 doi (DE-627)SPR047664711 (SPR)s00521-022-07119-2-e DE-627 ger DE-627 rakwb eng Liu, Pengjie verfasserin aut Dsa-PAML: a parallel automated machine learning system via dual-stacked autoencoder 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Finding a high-performance machine learning pipeline (ML pipeline) for a supervised learning task takes much time. It requires many choices, including preprocessing datasets, selecting algorithms, tuning hyperparameters, and ensembling candidate models. With increasing pipelines arises a combination explosion problem. This work presents a new automated machine learning (AutoML) system called Dsa-PAML to address this challenge by recommending, training, and ensembling suitable models for supervised learning tasks. Dsa-PAML is a parallel automated system based on a dual-stacked autoencoder (Dsa). Firstly, meta-features of datasets and ML pipelines are used to alleviate cold-start recommendation problems. Secondly, a novel dual-stacked autoencoder is used to simultaneously learn the latent features of datasets and ML pipelines, efficiently learning collaborations of both datasets and ML pipelines and recommending suitable ML pipelines for a new dataset. Thirdly, Dsa-PAML can train the recommended ML pipelines on the new dataset in a parallel method, which substantially reduces the time complexity of the proposed method. Finally, a parallel selective ensemble system is embedded into Dsa-PAML. It selects base models from candidate ML pipelines according to their runtime, classification performance, and diversity on the validation set, enhancing Dsa-PAML’s stability for most datasets. Amounts of experiments on 30 UCI datasets show that our approach outperforms current state-of-the-art methods. Automated machine learning (dpeaa)DE-He213 Parallel automated system (dpeaa)DE-He213 Dual-stacked autoencoder (dpeaa)DE-He213 Selective ensemble (dpeaa)DE-He213 Pan, Fucheng aut Zhou, Xiaofeng (orcid)0000-0001-9837-1261 aut Li, Shuai aut Zeng, Pengyu aut Liu, Shurui aut Jin, Liang aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 15 vom: 28. März, Seite 12985-13006 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:15 day:28 month:03 pages:12985-13006 https://dx.doi.org/10.1007/s00521-022-07119-2 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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 34 2022 15 28 03 12985-13006 |
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Liu, Pengjie @@aut@@ Pan, Fucheng @@aut@@ Zhou, Xiaofeng @@aut@@ Li, Shuai @@aut@@ Zeng, Pengyu @@aut@@ Liu, Shurui @@aut@@ Jin, Liang @@aut@@ |
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dsa-paml: a parallel automated machine learning system via dual-stacked autoencoder |
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Dsa-PAML: a parallel automated machine learning system via dual-stacked autoencoder |
abstract |
Abstract Finding a high-performance machine learning pipeline (ML pipeline) for a supervised learning task takes much time. It requires many choices, including preprocessing datasets, selecting algorithms, tuning hyperparameters, and ensembling candidate models. With increasing pipelines arises a combination explosion problem. This work presents a new automated machine learning (AutoML) system called Dsa-PAML to address this challenge by recommending, training, and ensembling suitable models for supervised learning tasks. Dsa-PAML is a parallel automated system based on a dual-stacked autoencoder (Dsa). Firstly, meta-features of datasets and ML pipelines are used to alleviate cold-start recommendation problems. Secondly, a novel dual-stacked autoencoder is used to simultaneously learn the latent features of datasets and ML pipelines, efficiently learning collaborations of both datasets and ML pipelines and recommending suitable ML pipelines for a new dataset. Thirdly, Dsa-PAML can train the recommended ML pipelines on the new dataset in a parallel method, which substantially reduces the time complexity of the proposed method. Finally, a parallel selective ensemble system is embedded into Dsa-PAML. It selects base models from candidate ML pipelines according to their runtime, classification performance, and diversity on the validation set, enhancing Dsa-PAML’s stability for most datasets. Amounts of experiments on 30 UCI datasets show that our approach outperforms current state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
abstractGer |
Abstract Finding a high-performance machine learning pipeline (ML pipeline) for a supervised learning task takes much time. It requires many choices, including preprocessing datasets, selecting algorithms, tuning hyperparameters, and ensembling candidate models. With increasing pipelines arises a combination explosion problem. This work presents a new automated machine learning (AutoML) system called Dsa-PAML to address this challenge by recommending, training, and ensembling suitable models for supervised learning tasks. Dsa-PAML is a parallel automated system based on a dual-stacked autoencoder (Dsa). Firstly, meta-features of datasets and ML pipelines are used to alleviate cold-start recommendation problems. Secondly, a novel dual-stacked autoencoder is used to simultaneously learn the latent features of datasets and ML pipelines, efficiently learning collaborations of both datasets and ML pipelines and recommending suitable ML pipelines for a new dataset. Thirdly, Dsa-PAML can train the recommended ML pipelines on the new dataset in a parallel method, which substantially reduces the time complexity of the proposed method. Finally, a parallel selective ensemble system is embedded into Dsa-PAML. It selects base models from candidate ML pipelines according to their runtime, classification performance, and diversity on the validation set, enhancing Dsa-PAML’s stability for most datasets. Amounts of experiments on 30 UCI datasets show that our approach outperforms current state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
abstract_unstemmed |
Abstract Finding a high-performance machine learning pipeline (ML pipeline) for a supervised learning task takes much time. It requires many choices, including preprocessing datasets, selecting algorithms, tuning hyperparameters, and ensembling candidate models. With increasing pipelines arises a combination explosion problem. This work presents a new automated machine learning (AutoML) system called Dsa-PAML to address this challenge by recommending, training, and ensembling suitable models for supervised learning tasks. Dsa-PAML is a parallel automated system based on a dual-stacked autoencoder (Dsa). Firstly, meta-features of datasets and ML pipelines are used to alleviate cold-start recommendation problems. Secondly, a novel dual-stacked autoencoder is used to simultaneously learn the latent features of datasets and ML pipelines, efficiently learning collaborations of both datasets and ML pipelines and recommending suitable ML pipelines for a new dataset. Thirdly, Dsa-PAML can train the recommended ML pipelines on the new dataset in a parallel method, which substantially reduces the time complexity of the proposed method. Finally, a parallel selective ensemble system is embedded into Dsa-PAML. It selects base models from candidate ML pipelines according to their runtime, classification performance, and diversity on the validation set, enhancing Dsa-PAML’s stability for most datasets. Amounts of experiments on 30 UCI datasets show that our approach outperforms current state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
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15 |
title_short |
Dsa-PAML: a parallel automated machine learning system via dual-stacked autoencoder |
url |
https://dx.doi.org/10.1007/s00521-022-07119-2 |
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author2 |
Pan, Fucheng Zhou, Xiaofeng Li, Shuai Zeng, Pengyu Liu, Shurui Jin, Liang |
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Pan, Fucheng Zhou, Xiaofeng Li, Shuai Zeng, Pengyu Liu, Shurui Jin, Liang |
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doi_str |
10.1007/s00521-022-07119-2 |
up_date |
2024-07-03T14:11:59.622Z |
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|
score |
7.397897 |