Ensemble of M5 Model Tree Based Modelling of Sodium Adsorption Ratio
This work reports the results of four ensemble approaches with the M5 model tree as the base regression model to anticipate Sodium Adsorption Ratio (SAR). Ensemble methods that combine the output of multiple regression models have been found to be more accurate than any of the individual models maki...
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| Format: | Article |
| Language: | English |
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Shahrood University of Technology
2018-03-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
| Subjects: | |
| Online Access: | http://jad.shahroodut.ac.ir/article_1015_957fdfdc9de0cc89dbb4339ccf806dc4.pdf |
| _version_ | 1819172090609139712 |
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| author | M. T. Sattari M. Pal R. Mirabbasi J. Abraham |
| author_facet | M. T. Sattari M. Pal R. Mirabbasi J. Abraham |
| author_sort | M. T. Sattari |
| collection | DOAJ |
| description | This work reports the results of four ensemble approaches with the M5 model tree as the base regression model to anticipate Sodium Adsorption Ratio (SAR). Ensemble methods that combine the output of multiple regression models have been found to be more accurate than any of the individual models making up the ensemble. In this study additive boosting, bagging, rotation forest and random subspace methods are used. The dataset, which consisted of 488 samples with nine input parameters were obtained from the Barandoozchay River in West Azerbaijan province, Iran. Three evaluation criteria: correlation coefficient, root mean square error and mean absolute error were used to judge the accuracy of different ensemble models. In addition to the use of M5 model tree to predict the SAR values, a wrapper-based variable selection approach using a M5 model tree as the learning algorithm and a genetic algorithm, was also used to select useful input variables. The encouraging performance motivates the use of this technique to predict SAR values. |
| first_indexed | 2024-12-22T20:01:39Z |
| format | Article |
| id | doaj.art-a922ab2e3e7e40e5a8dafb809e692a2f |
| institution | Directory Open Access Journal |
| issn | 2322-5211 2322-4444 |
| language | English |
| last_indexed | 2024-12-22T20:01:39Z |
| publishDate | 2018-03-01 |
| publisher | Shahrood University of Technology |
| record_format | Article |
| series | Journal of Artificial Intelligence and Data Mining |
| spelling | doaj.art-a922ab2e3e7e40e5a8dafb809e692a2f2022-12-21T18:14:16ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442018-03-0161697810.22044/jadm.2017.5540.16631015Ensemble of M5 Model Tree Based Modelling of Sodium Adsorption RatioM. T. Sattari0M. Pal1R. Mirabbasi2J. Abraham3Department of Water Engineering, Agriculture Faculty, University of Tabriz, Tabriz, Iran.Department of Civil Engineering, National Institute of Technology, Kurukshetra, 136119, Haryana, India.Department of Water Engineering, Agriculture Faculty, University of Shahrekord, Shahrekord, Iran.University of St. Thomas, School of Engineering, 2115 Summit Ave, St. Paul, MN 55105-1079, USA.This work reports the results of four ensemble approaches with the M5 model tree as the base regression model to anticipate Sodium Adsorption Ratio (SAR). Ensemble methods that combine the output of multiple regression models have been found to be more accurate than any of the individual models making up the ensemble. In this study additive boosting, bagging, rotation forest and random subspace methods are used. The dataset, which consisted of 488 samples with nine input parameters were obtained from the Barandoozchay River in West Azerbaijan province, Iran. Three evaluation criteria: correlation coefficient, root mean square error and mean absolute error were used to judge the accuracy of different ensemble models. In addition to the use of M5 model tree to predict the SAR values, a wrapper-based variable selection approach using a M5 model tree as the learning algorithm and a genetic algorithm, was also used to select useful input variables. The encouraging performance motivates the use of this technique to predict SAR values.http://jad.shahroodut.ac.ir/article_1015_957fdfdc9de0cc89dbb4339ccf806dc4.pdfSodium Adsorption Ratio (SAR)data miningM5 model treeGenetic algorithmWrapper Approach |
| spellingShingle | M. T. Sattari M. Pal R. Mirabbasi J. Abraham Ensemble of M5 Model Tree Based Modelling of Sodium Adsorption Ratio Journal of Artificial Intelligence and Data Mining Sodium Adsorption Ratio (SAR) data mining M5 model tree Genetic algorithm Wrapper Approach |
| title | Ensemble of M5 Model Tree Based Modelling of Sodium Adsorption Ratio |
| title_full | Ensemble of M5 Model Tree Based Modelling of Sodium Adsorption Ratio |
| title_fullStr | Ensemble of M5 Model Tree Based Modelling of Sodium Adsorption Ratio |
| title_full_unstemmed | Ensemble of M5 Model Tree Based Modelling of Sodium Adsorption Ratio |
| title_short | Ensemble of M5 Model Tree Based Modelling of Sodium Adsorption Ratio |
| title_sort | ensemble of m5 model tree based modelling of sodium adsorption ratio |
| topic | Sodium Adsorption Ratio (SAR) data mining M5 model tree Genetic algorithm Wrapper Approach |
| url | http://jad.shahroodut.ac.ir/article_1015_957fdfdc9de0cc89dbb4339ccf806dc4.pdf |
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