Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline
In this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of the dataset from the Kaggle database through...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/14/4/232 |
| _version_ | 1797604999866351616 |
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| author | Konstantinos Filippou George Aifantis George A. Papakostas George E. Tsekouras |
| author_facet | Konstantinos Filippou George Aifantis George A. Papakostas George E. Tsekouras |
| author_sort | Konstantinos Filippou |
| collection | DOAJ |
| description | In this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of the dataset from the Kaggle database through the Kaggle API. The second utilizes the Keras-Bayesian optimization tuning library to perform hyperparameter optimization. The third focuses on the training process of the machine learning (ML) model using the hyperparameter values estimated in the previous stage, and its evaluation is performed on the testing data by implementing the Neptune AI. The main technologies used to develop a stable and reusable machine learning pipeline are the popular Git version control system, the Google cloud virtual machine, the Jenkins server, the Docker containerization technology, and the Ngrok reverse proxy tool. The latter can securely publish the local Jenkins address as public through the internet. As such, some parts of the proposed pipeline are taken from the thematic area of machine learning operations (MLOps), resulting in a hybrid software scheme. The machine learning model was used to evaluate the pipeline, which is a multilayer perceptron (MLP) that combines typical dense, as well as polynomial, layers. The simulation results show that the proposed pipeline exhibits a reliable and accurate performance while managing to boost the network’s performance in classification tasks. |
| first_indexed | 2024-03-11T04:54:50Z |
| format | Article |
| id | doaj.art-30f54cbfe6d042da91e3b6eb58bec586 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-11T04:54:50Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-30f54cbfe6d042da91e3b6eb58bec5862023-11-17T19:44:33ZengMDPI AGInformation2078-24892023-04-0114423210.3390/info14040232Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) PipelineKonstantinos Filippou0George Aifantis1George A. Papakostas2George E. Tsekouras3Computational Intelligence (CI) Research Group, Department of Cultural Technology and Communications, University of the Aegean, 81100 Mytilene, GreeceComputational Intelligence (CI) Research Group, Department of Cultural Technology and Communications, University of the Aegean, 81100 Mytilene, GreeceMLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, GreeceComputational Intelligence (CI) Research Group, Department of Cultural Technology and Communications, University of the Aegean, 81100 Mytilene, GreeceIn this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of the dataset from the Kaggle database through the Kaggle API. The second utilizes the Keras-Bayesian optimization tuning library to perform hyperparameter optimization. The third focuses on the training process of the machine learning (ML) model using the hyperparameter values estimated in the previous stage, and its evaluation is performed on the testing data by implementing the Neptune AI. The main technologies used to develop a stable and reusable machine learning pipeline are the popular Git version control system, the Google cloud virtual machine, the Jenkins server, the Docker containerization technology, and the Ngrok reverse proxy tool. The latter can securely publish the local Jenkins address as public through the internet. As such, some parts of the proposed pipeline are taken from the thematic area of machine learning operations (MLOps), resulting in a hybrid software scheme. The machine learning model was used to evaluate the pipeline, which is a multilayer perceptron (MLP) that combines typical dense, as well as polynomial, layers. The simulation results show that the proposed pipeline exhibits a reliable and accurate performance while managing to boost the network’s performance in classification tasks.https://www.mdpi.com/2078-2489/14/4/232AutoMLMLOpsstructure learninghyperparameter optimizationBayesian optimizationmultilayer perceptron |
| spellingShingle | Konstantinos Filippou George Aifantis George A. Papakostas George E. Tsekouras Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline Information AutoML MLOps structure learning hyperparameter optimization Bayesian optimization multilayer perceptron |
| title | Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline |
| title_full | Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline |
| title_fullStr | Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline |
| title_full_unstemmed | Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline |
| title_short | Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline |
| title_sort | structure learning and hyperparameter optimization using an automated machine learning automl pipeline |
| topic | AutoML MLOps structure learning hyperparameter optimization Bayesian optimization multilayer perceptron |
| url | https://www.mdpi.com/2078-2489/14/4/232 |
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