مقارنة خوارزميات التكييس والتعزيز في تعليم المجموعات (Ensemble learning) للتنبؤ بأمراض القلب
الكلمات المفتاحية:
آلات تعزيز التدرج، آلة تعزيز التدرج الخفيف، تعزيز التدرج الأقصى، مقياس نسبة صحة، مقياس المثالية، مقياس الدقة، تعليم المجموعاتالملخص
إن تعليم المجموعات (Ensemble learning) هو نهج عام لوصف التعلم الآلي يسعى إلى أداء تنبؤي أفضل من خلال الجمع بين التنبؤات من نماذج متعددة. يتضمن أسلوب تعليم المجموعات عددًا من الطرق منها أسلوب التكييس (Bagging) والتعزيز(Boosting) ويمتلك هذين الاسلوبين مجموعة من الخوارزميات منها خوارزمية الغابة العشوائية Random Force وخوارزمية التدرج التكيفي AdaBoost وخورزميات التدرج التعزيزي (Gradient Boosting). في هذا البحث، سنقارن بين الاسلوبين التكييس والتعزيز من حيث نسبة صحة خوارزمية التصنيف (Accuracy) ومقياس المثالية (Recall) ومقياس الدقة (Precision) معامل Cohen Kappa ومقياس F (F-measure) ومقياس الحساسية (Sensitivity) ومقياس النوعية (Specificity) ومستوى المنطقة الواقعة تحت منحنىROC في التنبؤ بأمراض قصور القلب.
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