Modify Random Forest Algorithm Using Hybrid Feature Selection Method

Abstract

The Importance of Random Forrest(RF) is one of the most powerful ‎methods ‎of ‎machine learning in ‎Decision Tree.‎ The Proposed hybrid feature selection for Random Forest depend on ‎two ‎measure ‎‎Information Gain and Gini Index in varying percentages ‎based on ‎weight.‎ In this paper, we tend to ‎propose a modify Random Forrest‏ ‏‎algorithm named ‎Random Forest algorithm using hybrid ‎feature ‎‎selection ‎that uses hybrid feature ‎selection instead of ‎using ‎one feature selection. The ‎main plan is to ‎computation the ‎‎ Information ‎Gain for all random selection ‎feature then search for ‎the best split ‎‎point in ‎the node that gives the best ‎value for a hybrid ‎equation with ‎Gini Index. ‎The experimental results on the ‎dataset ‎showed that the proposed ‎modification is ‎better than the classic Random ‎Forest compared to ‎the standard static Random ‎Forest the hybrid feature ‎‎selection Random Forrest shows significant ‎improvement ‎in accuracy measure.‎