Enhancement of Data Classification Accuracy using Bagging Technique in Random Forest - Vikas, S; SN, Thimmaraju (International Journal of Computer Sciences and Engineering, 2019)
Random forest are able to do classification on high performance through a classification ensemble with a decision trees that grow mistreatment at random elect subspaces of information. The performance of associate degree ensemble learner is very obsessed on the accuracy of every element learner and also the diversity among these parts. In random forest, organisation would cause incidence of unhealthy trees and should embrace related trees. This ends up in inappropriate and poor ensemble classification call. During this paper a shot has been created to enhance the performance of the model by applying material technique in a very random forest. Experimental results have shown that, the random forest are often more increased in terms of the classification accuracy.’