CF-DAML: Distributed automated machine learning based on collaborative filtering
Abstract The search for a good machine learning (ML) model takes a long time and requires the considerations of many alternatives, including data preprocessing, algorithm selection, and hyperparameter tuning methods. Thus, tedious searches face a combinatorial explosion problem. In this work, we bui...
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Gespeichert in:
| Autor*in: |
Liu, Pengjie [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
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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 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 52(2022), 15 vom: 31. März, Seite 17145-17169 |
|---|---|
| Übergeordnetes Werk: |
volume:52 ; year:2022 ; number:15 ; day:31 ; month:03 ; pages:17145-17169 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10489-021-03049-z |
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OLC2080016865 |
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| 520 | |a Abstract The search for a good machine learning (ML) model takes a long time and requires the considerations of many alternatives, including data preprocessing, algorithm selection, and hyperparameter tuning methods. Thus, tedious searches face a combinatorial explosion problem. In this work, we build a new automated machine learning (AutoML) system called CF-DAML, a distributed automated system based on collaborative filtering (CF), to address these challenges by recommending and training suitable models for supervised learning tasks. CF-DAML first computes some informative meta-features for a new dataset, then uses a weighted $$l_1$$-norm (W1-norm) to accurately calculate the k nearest neighbors (kNN) of the new dataset, and finally recommends the top N models with good performances on each of its neighbors to the new dataset. We also design a distributed system (DSTM) for training the models to reduce the time complexity substantially. In addition, we develop a multilayer selective stacked ensemble system (MSSE), whose base models are selected from among suitable candidate models based on their runtimes, classification accuracies, and diversities, to enhance the stability of CF-DAML. To our knowledge, this is the first work to combine memory-based CF and the selective stacked ensemble to solve the AutoML problem. Extensive experiments are conducted on many UCI datasets and the comparative results demonstrate that our approach outperforms the current state-of-the-art methods. | ||
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| 700 | 1 | |a Li, Shuai |4 aut | |
| 700 | 1 | |a Jin, Liang |4 aut | |
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10.1007/s10489-021-03049-z doi (DE-627)OLC2080016865 (DE-He213)s10489-021-03049-z-p DE-627 ger DE-627 rakwb eng 004 VZ Liu, Pengjie verfasserin aut CF-DAML: Distributed automated machine learning based on collaborative filtering 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The search for a good machine learning (ML) model takes a long time and requires the considerations of many alternatives, including data preprocessing, algorithm selection, and hyperparameter tuning methods. Thus, tedious searches face a combinatorial explosion problem. In this work, we build a new automated machine learning (AutoML) system called CF-DAML, a distributed automated system based on collaborative filtering (CF), to address these challenges by recommending and training suitable models for supervised learning tasks. CF-DAML first computes some informative meta-features for a new dataset, then uses a weighted $$l_1$$-norm (W1-norm) to accurately calculate the k nearest neighbors (kNN) of the new dataset, and finally recommends the top N models with good performances on each of its neighbors to the new dataset. We also design a distributed system (DSTM) for training the models to reduce the time complexity substantially. In addition, we develop a multilayer selective stacked ensemble system (MSSE), whose base models are selected from among suitable candidate models based on their runtimes, classification accuracies, and diversities, to enhance the stability of CF-DAML. To our knowledge, this is the first work to combine memory-based CF and the selective stacked ensemble to solve the AutoML problem. Extensive experiments are conducted on many UCI datasets and the comparative results demonstrate that our approach outperforms the current state-of-the-art methods. Automated machine learning Collaborative filtering Weighted -norm Distributed automated system Multilayer selective stacked ensemble Pan, Fucheng aut Zhou, Xiaofeng (orcid)0000-0001-9837-1261 aut Li, Shuai aut Jin, Liang aut Enthalten in Applied intelligence Springer US, 1991 52(2022), 15 vom: 31. März, Seite 17145-17169 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2022 number:15 day:31 month:03 pages:17145-17169 https://doi.org/10.1007/s10489-021-03049-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2022 15 31 03 17145-17169 |
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10.1007/s10489-021-03049-z doi (DE-627)OLC2080016865 (DE-He213)s10489-021-03049-z-p DE-627 ger DE-627 rakwb eng 004 VZ Liu, Pengjie verfasserin aut CF-DAML: Distributed automated machine learning based on collaborative filtering 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The search for a good machine learning (ML) model takes a long time and requires the considerations of many alternatives, including data preprocessing, algorithm selection, and hyperparameter tuning methods. Thus, tedious searches face a combinatorial explosion problem. In this work, we build a new automated machine learning (AutoML) system called CF-DAML, a distributed automated system based on collaborative filtering (CF), to address these challenges by recommending and training suitable models for supervised learning tasks. CF-DAML first computes some informative meta-features for a new dataset, then uses a weighted $$l_1$$-norm (W1-norm) to accurately calculate the k nearest neighbors (kNN) of the new dataset, and finally recommends the top N models with good performances on each of its neighbors to the new dataset. We also design a distributed system (DSTM) for training the models to reduce the time complexity substantially. In addition, we develop a multilayer selective stacked ensemble system (MSSE), whose base models are selected from among suitable candidate models based on their runtimes, classification accuracies, and diversities, to enhance the stability of CF-DAML. To our knowledge, this is the first work to combine memory-based CF and the selective stacked ensemble to solve the AutoML problem. Extensive experiments are conducted on many UCI datasets and the comparative results demonstrate that our approach outperforms the current state-of-the-art methods. Automated machine learning Collaborative filtering Weighted -norm Distributed automated system Multilayer selective stacked ensemble Pan, Fucheng aut Zhou, Xiaofeng (orcid)0000-0001-9837-1261 aut Li, Shuai aut Jin, Liang aut Enthalten in Applied intelligence Springer US, 1991 52(2022), 15 vom: 31. März, Seite 17145-17169 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2022 number:15 day:31 month:03 pages:17145-17169 https://doi.org/10.1007/s10489-021-03049-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2022 15 31 03 17145-17169 |
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10.1007/s10489-021-03049-z doi (DE-627)OLC2080016865 (DE-He213)s10489-021-03049-z-p DE-627 ger DE-627 rakwb eng 004 VZ Liu, Pengjie verfasserin aut CF-DAML: Distributed automated machine learning based on collaborative filtering 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The search for a good machine learning (ML) model takes a long time and requires the considerations of many alternatives, including data preprocessing, algorithm selection, and hyperparameter tuning methods. Thus, tedious searches face a combinatorial explosion problem. In this work, we build a new automated machine learning (AutoML) system called CF-DAML, a distributed automated system based on collaborative filtering (CF), to address these challenges by recommending and training suitable models for supervised learning tasks. CF-DAML first computes some informative meta-features for a new dataset, then uses a weighted $$l_1$$-norm (W1-norm) to accurately calculate the k nearest neighbors (kNN) of the new dataset, and finally recommends the top N models with good performances on each of its neighbors to the new dataset. We also design a distributed system (DSTM) for training the models to reduce the time complexity substantially. In addition, we develop a multilayer selective stacked ensemble system (MSSE), whose base models are selected from among suitable candidate models based on their runtimes, classification accuracies, and diversities, to enhance the stability of CF-DAML. To our knowledge, this is the first work to combine memory-based CF and the selective stacked ensemble to solve the AutoML problem. Extensive experiments are conducted on many UCI datasets and the comparative results demonstrate that our approach outperforms the current state-of-the-art methods. Automated machine learning Collaborative filtering Weighted -norm Distributed automated system Multilayer selective stacked ensemble Pan, Fucheng aut Zhou, Xiaofeng (orcid)0000-0001-9837-1261 aut Li, Shuai aut Jin, Liang aut Enthalten in Applied intelligence Springer US, 1991 52(2022), 15 vom: 31. März, Seite 17145-17169 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2022 number:15 day:31 month:03 pages:17145-17169 https://doi.org/10.1007/s10489-021-03049-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2022 15 31 03 17145-17169 |
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10.1007/s10489-021-03049-z doi (DE-627)OLC2080016865 (DE-He213)s10489-021-03049-z-p DE-627 ger DE-627 rakwb eng 004 VZ Liu, Pengjie verfasserin aut CF-DAML: Distributed automated machine learning based on collaborative filtering 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The search for a good machine learning (ML) model takes a long time and requires the considerations of many alternatives, including data preprocessing, algorithm selection, and hyperparameter tuning methods. Thus, tedious searches face a combinatorial explosion problem. In this work, we build a new automated machine learning (AutoML) system called CF-DAML, a distributed automated system based on collaborative filtering (CF), to address these challenges by recommending and training suitable models for supervised learning tasks. CF-DAML first computes some informative meta-features for a new dataset, then uses a weighted $$l_1$$-norm (W1-norm) to accurately calculate the k nearest neighbors (kNN) of the new dataset, and finally recommends the top N models with good performances on each of its neighbors to the new dataset. We also design a distributed system (DSTM) for training the models to reduce the time complexity substantially. In addition, we develop a multilayer selective stacked ensemble system (MSSE), whose base models are selected from among suitable candidate models based on their runtimes, classification accuracies, and diversities, to enhance the stability of CF-DAML. To our knowledge, this is the first work to combine memory-based CF and the selective stacked ensemble to solve the AutoML problem. Extensive experiments are conducted on many UCI datasets and the comparative results demonstrate that our approach outperforms the current state-of-the-art methods. Automated machine learning Collaborative filtering Weighted -norm Distributed automated system Multilayer selective stacked ensemble Pan, Fucheng aut Zhou, Xiaofeng (orcid)0000-0001-9837-1261 aut Li, Shuai aut Jin, Liang aut Enthalten in Applied intelligence Springer US, 1991 52(2022), 15 vom: 31. März, Seite 17145-17169 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2022 number:15 day:31 month:03 pages:17145-17169 https://doi.org/10.1007/s10489-021-03049-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2022 15 31 03 17145-17169 |
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10.1007/s10489-021-03049-z doi (DE-627)OLC2080016865 (DE-He213)s10489-021-03049-z-p DE-627 ger DE-627 rakwb eng 004 VZ Liu, Pengjie verfasserin aut CF-DAML: Distributed automated machine learning based on collaborative filtering 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The search for a good machine learning (ML) model takes a long time and requires the considerations of many alternatives, including data preprocessing, algorithm selection, and hyperparameter tuning methods. Thus, tedious searches face a combinatorial explosion problem. In this work, we build a new automated machine learning (AutoML) system called CF-DAML, a distributed automated system based on collaborative filtering (CF), to address these challenges by recommending and training suitable models for supervised learning tasks. CF-DAML first computes some informative meta-features for a new dataset, then uses a weighted $$l_1$$-norm (W1-norm) to accurately calculate the k nearest neighbors (kNN) of the new dataset, and finally recommends the top N models with good performances on each of its neighbors to the new dataset. We also design a distributed system (DSTM) for training the models to reduce the time complexity substantially. In addition, we develop a multilayer selective stacked ensemble system (MSSE), whose base models are selected from among suitable candidate models based on their runtimes, classification accuracies, and diversities, to enhance the stability of CF-DAML. To our knowledge, this is the first work to combine memory-based CF and the selective stacked ensemble to solve the AutoML problem. Extensive experiments are conducted on many UCI datasets and the comparative results demonstrate that our approach outperforms the current state-of-the-art methods. Automated machine learning Collaborative filtering Weighted -norm Distributed automated system Multilayer selective stacked ensemble Pan, Fucheng aut Zhou, Xiaofeng (orcid)0000-0001-9837-1261 aut Li, Shuai aut Jin, Liang aut Enthalten in Applied intelligence Springer US, 1991 52(2022), 15 vom: 31. März, Seite 17145-17169 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:52 year:2022 number:15 day:31 month:03 pages:17145-17169 https://doi.org/10.1007/s10489-021-03049-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 52 2022 15 31 03 17145-17169 |
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Liu, Pengjie |
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10.1007/s10489-021-03049-z |
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cf-daml: distributed automated machine learning based on collaborative filtering |
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CF-DAML: Distributed automated machine learning based on collaborative filtering |
| abstract |
Abstract The search for a good machine learning (ML) model takes a long time and requires the considerations of many alternatives, including data preprocessing, algorithm selection, and hyperparameter tuning methods. Thus, tedious searches face a combinatorial explosion problem. In this work, we build a new automated machine learning (AutoML) system called CF-DAML, a distributed automated system based on collaborative filtering (CF), to address these challenges by recommending and training suitable models for supervised learning tasks. CF-DAML first computes some informative meta-features for a new dataset, then uses a weighted $$l_1$$-norm (W1-norm) to accurately calculate the k nearest neighbors (kNN) of the new dataset, and finally recommends the top N models with good performances on each of its neighbors to the new dataset. We also design a distributed system (DSTM) for training the models to reduce the time complexity substantially. In addition, we develop a multilayer selective stacked ensemble system (MSSE), whose base models are selected from among suitable candidate models based on their runtimes, classification accuracies, and diversities, to enhance the stability of CF-DAML. To our knowledge, this is the first work to combine memory-based CF and the selective stacked ensemble to solve the AutoML problem. Extensive experiments are conducted on many UCI datasets and the comparative results demonstrate that our approach outperforms the current state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract The search for a good machine learning (ML) model takes a long time and requires the considerations of many alternatives, including data preprocessing, algorithm selection, and hyperparameter tuning methods. Thus, tedious searches face a combinatorial explosion problem. In this work, we build a new automated machine learning (AutoML) system called CF-DAML, a distributed automated system based on collaborative filtering (CF), to address these challenges by recommending and training suitable models for supervised learning tasks. CF-DAML first computes some informative meta-features for a new dataset, then uses a weighted $$l_1$$-norm (W1-norm) to accurately calculate the k nearest neighbors (kNN) of the new dataset, and finally recommends the top N models with good performances on each of its neighbors to the new dataset. We also design a distributed system (DSTM) for training the models to reduce the time complexity substantially. In addition, we develop a multilayer selective stacked ensemble system (MSSE), whose base models are selected from among suitable candidate models based on their runtimes, classification accuracies, and diversities, to enhance the stability of CF-DAML. To our knowledge, this is the first work to combine memory-based CF and the selective stacked ensemble to solve the AutoML problem. Extensive experiments are conducted on many UCI datasets and the comparative results demonstrate that our approach outperforms the current state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract The search for a good machine learning (ML) model takes a long time and requires the considerations of many alternatives, including data preprocessing, algorithm selection, and hyperparameter tuning methods. Thus, tedious searches face a combinatorial explosion problem. In this work, we build a new automated machine learning (AutoML) system called CF-DAML, a distributed automated system based on collaborative filtering (CF), to address these challenges by recommending and training suitable models for supervised learning tasks. CF-DAML first computes some informative meta-features for a new dataset, then uses a weighted $$l_1$$-norm (W1-norm) to accurately calculate the k nearest neighbors (kNN) of the new dataset, and finally recommends the top N models with good performances on each of its neighbors to the new dataset. We also design a distributed system (DSTM) for training the models to reduce the time complexity substantially. In addition, we develop a multilayer selective stacked ensemble system (MSSE), whose base models are selected from among suitable candidate models based on their runtimes, classification accuracies, and diversities, to enhance the stability of CF-DAML. To our knowledge, this is the first work to combine memory-based CF and the selective stacked ensemble to solve the AutoML problem. Extensive experiments are conducted on many UCI datasets and the comparative results demonstrate that our approach outperforms the current state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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CF-DAML: Distributed automated machine learning based on collaborative filtering |
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Pan, Fucheng Zhou, Xiaofeng Li, Shuai Jin, Liang |
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Pan, Fucheng Zhou, Xiaofeng Li, Shuai Jin, Liang |
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