Performance Assessment of Random Forest Induction Methods

In this paper, we propose a performance assessment of a forest induction method called IDTNIM-RF which uses the IDT_NIM “Induction of Decision Tree New Information Measure” for the trees induction. The specific techniques applied to forests such as bagging and random feature selection are used for generating multiple IDT _ NIM trees. We compare the IDTNIM-RF method with a RF random forest that uses CART as a basic rule. The success rate criterion of the single CART and IDT_NIM trees used respectively to generate URF and IDTNIM - RF forest sets is used as a performance assessment basis. To achieve this evaluation, data sets are carried from the UCI Repository and some learning bases we have already developed such MONITDIAB and COVID_EHU.