Ensemble of M5 Model Tree Based Modelling of Sodium Adsorption Ratio
محل انتشار: مجله هوش مصنوعی و داده کاوی، دوره: 6، شماره: 1
سال انتشار: 1397
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 336
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شناسه ملی سند علمی:
JR_JADM-6-1_006
تاریخ نمایه سازی: 19 تیر 1398
چکیده مقاله:
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.
کلیدواژه ها:
نویسندگان
M. T. Sattari
Department of Water Engineering, Agriculture Faculty, University of Tabriz, Tabriz, Iran.
M. Pal
Department of Civil Engineering, National Institute of Technology, Kurukshetra, ۱۳۶۱۱۹, Haryana, India.
R. Mirabbasi
Department of Water Engineering, Agriculture Faculty, University of Shahrekord, Shahrekord, Iran.
J. Abraham
University of St. Thomas, School of Engineering, ۲۱۱۵ Summit Ave, St. Paul, MN ۵۵۱۰۵-۱۰۷۹, USA.