Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics
Machine learning techniques have been used to develop many regression models to make predictions based on experience and historical data. They might be used singly or in ensembles. Single models are either classification or regression models that use one technique, while ensemble models combine vari...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/6/686 |
| _version_ | 1797540372467941376 |
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| author | Jui-Sheng Chou Dinh-Nhat Truong Chih-Fong Tsai |
| author_facet | Jui-Sheng Chou Dinh-Nhat Truong Chih-Fong Tsai |
| author_sort | Jui-Sheng Chou |
| collection | DOAJ |
| description | Machine learning techniques have been used to develop many regression models to make predictions based on experience and historical data. They might be used singly or in ensembles. Single models are either classification or regression models that use one technique, while ensemble models combine various single models. To construct or find the best model is very complex and time-consuming, so this study develops a new platform, called intelligent Machine Learner (iML), to automatically build popular models and identify the best one. The iML platform is benchmarked with WEKA by analyzing publicly available datasets. After that, four industrial experiments are conducted to evaluate the performance of iML. In all cases, the best models determined by iML are superior to prior studies in terms of accuracy and computation time. Thus, the iML is a powerful and efficient tool for solving regression problems in engineering informatics. |
| first_indexed | 2024-03-10T13:00:11Z |
| format | Article |
| id | doaj.art-a10f0f1cc9504a4ca6d8fd1b69864711 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T13:00:11Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-a10f0f1cc9504a4ca6d8fd1b698647112023-11-21T11:37:35ZengMDPI AGMathematics2227-73902021-03-019668610.3390/math9060686Solving Regression Problems with Intelligent Machine Learner for Engineering InformaticsJui-Sheng Chou0Dinh-Nhat Truong1Chih-Fong Tsai2Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei City 106335, TaiwanDepartment of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei City 106335, TaiwanDepartment of Information Management, National Central University, Taoyuan City 320317, TaiwanMachine learning techniques have been used to develop many regression models to make predictions based on experience and historical data. They might be used singly or in ensembles. Single models are either classification or regression models that use one technique, while ensemble models combine various single models. To construct or find the best model is very complex and time-consuming, so this study develops a new platform, called intelligent Machine Learner (iML), to automatically build popular models and identify the best one. The iML platform is benchmarked with WEKA by analyzing publicly available datasets. After that, four industrial experiments are conducted to evaluate the performance of iML. In all cases, the best models determined by iML are superior to prior studies in terms of accuracy and computation time. Thus, the iML is a powerful and efficient tool for solving regression problems in engineering informatics.https://www.mdpi.com/2227-7390/9/6/686applied machine learningclassification and regressiondata miningensemble modelengineering informatics |
| spellingShingle | Jui-Sheng Chou Dinh-Nhat Truong Chih-Fong Tsai Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics Mathematics applied machine learning classification and regression data mining ensemble model engineering informatics |
| title | Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics |
| title_full | Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics |
| title_fullStr | Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics |
| title_full_unstemmed | Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics |
| title_short | Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics |
| title_sort | solving regression problems with intelligent machine learner for engineering informatics |
| topic | applied machine learning classification and regression data mining ensemble model engineering informatics |
| url | https://www.mdpi.com/2227-7390/9/6/686 |
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