TY - JOUR ID - 1015 TI - Ensemble of M5 Model Tree Based Modelling of Sodium Adsorption Ratio JO - Journal of AI and Data Mining JA - JADM LA - en SN - 2322-5211 AU - Sattari, M. T. AU - Pal, M. AU - Mirabbasi, R. AU - Abraham, J. AD - Department of Water Engineering, Agriculture Faculty, University of Tabriz, Tabriz, Iran. AD - Department of Civil Engineering, National Institute of Technology, Kurukshetra, 136119, Haryana, India. AD - Department of Water Engineering, Agriculture Faculty, University of Shahrekord, Shahrekord, Iran. AD - University of St. Thomas, School of Engineering, 2115 Summit Ave, St. Paul, MN 55105-1079, USA. Y1 - 2018 PY - 2018 VL - 6 IS - 1 SP - 69 EP - 78 KW - Sodium Adsorption Ratio (SAR) KW - data mining KW - M5 model tree KW - Genetic Algorithm KW - Wrapper Approach DO - 10.22044/jadm.2017.5540.1663 N2 - 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. UR - https://jad.shahroodut.ac.ir/article_1015.html L1 - https://jad.shahroodut.ac.ir/article_1015_957fdfdc9de0cc89dbb4339ccf806dc4.pdf ER -