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سعید سلطانی محمدی

سعید سلطانی محمدی

دانشیار

دانشکده: دانشکده مهـندسـی

گروه: مهندسی معدن

مقطع تحصیلی: دکترای تخصصی

سال تولد: ۱۳۶۰

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سعید سلطانی محمدی

دانشیار سعید سلطانی محمدی

دانشکده: دانشکده مهـندسـی - گروه: مهندسی معدن مقطع تحصیلی: دکترای تخصصی | سال تولد: ۱۳۶۰ |

دانشجویان عزیز با توجه به تمرکز جلسات در برخی از ساعات اعلام شده به عنوان امور اجرایی، پیش از مراجعه خضوری از طریق تماس صوتی یا پیامی هماهنگ فرمایید. 

نمایش بیشتر

Localizing the base learner weights in ensemble methods to improve the grade modeling accuracy

نویسندگاناحمدرضا عرفان,سعید سلطانی محمدی,ملیحه عباس زاده
نشریهJournal of Mining and Environment
ضریب تاثیر (IF)ثبت نشده
نوع مقالهFull Paper
تاریخ انتشار0000-00-00
رتبه نشریهعلمی - پژوهشی
نوع نشریهالکترونیکی
کشور محل چاپایران
نمایه نشریهISC ,JCR ,SCOPUS

چکیده مقاله

Machine learning (ML) has significantly transformed multiple disciplines, including mineral resource evaluation in mining engineering, by facilitating more accurate and efficient estimation methods. Ensemble methods, as a fundamental component of modern machine learning, have emerged as powerful tools that robust techniques that integrate multiple predictive models to improve performance beyond that of any individual learner. This study proposes a novel ensemble method for estimating ore grades by localizing the base learner weights in ensemble method. Ordinary kriging, inverse distance weighting, k-nearest neighbors, support vector regression, and artificial neural networks have been used as the base learners of the algorithm. In ML base learners, coordinates (easting, northing and elevation) of samples have been defined as input nodes and grade has been defined as target. The proposed method has been validated for predicting the copper grade (Cu%) in Darehzar porphyry deposit. The performance of proposed method has been by individual base learners and famous ensemble methods. This comparison shows that performance of proposed method is better than other ones. The findings highlight the necessity of adapting ensemble methods to address spatial variability in geological data, thereby establishing a robust framework for ore grade estimation.