K-Ortalama Kümelerinin Sınıf Bilgisi Olarak Karar Ağacı Oluşturmada Kullanılması ve Glokom Çoklu Sınıflandırılmasında Başarıma Etkisi
Bu çalışma çoklu sınıflandırmada performans artırımı için K-Ortalama ve Karar Ağacı yöntemlerinden oluşan bir model sunmaktadır. Model glukom veri kümesi üzerinde test edilmiş kesinlik ölçütü 0,808, ROC alanı 0,839 bulunmuştur.
Usage Of K-Means Clusters as Class Labes In Decısıon Trees and Its Effect On Multıclassıfıcatıon Performance Of Glaucoma
In this study a model of K-Means - Decision Tree is presented to increase the multiclassification performance. This model is tested on glaucoma dataset, the accuracy and the are under ROC curve is calculated as 0.808, 0.839 respectively.
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