Custom Loss Functions in XGBoost Algorithm for Enhanced Critical Error Mitigation in Drill-Wear Analysis of Melamine-Faced Chipboard

The advancement of machine learning in industrial applications has necessitated the development of tailored solutions to address specific challenges, particularly in multi-class classification tasks. This study delves into the customization of loss functions within the eXtreme Gradient Boosting (XGB...

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Principais autores:	Michał Bukowski, Jarosław Kurek, Bartosz Świderski, Albina Jegorowa
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	MDPI AG 2024-02-01
סדרה:	Sensors
נושאים:	drill-wear analysis in woodworking XGBoost custom loss functions melamine-faced chipboard machining critical error reduction in classification
גישה מקוונת:	https://www.mdpi.com/1424-8220/24/4/1092
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!