A Random Forest Classification Algorithm Based on Dichotomy Rule Fusion

Abstract

The classical random forest algorithm has associated features and bias problems, which leads to a reduction in classification accuracy, in this paper we propose a random forest classification algorithm based on dichotomy rule fusion. The dichotomy rule fusion method is based on the idea of information gain and recursive feature elimination to select a better feature sequence, which improves the classification accuracy. Experimental results on international standard data sets show that the algorithm has better performance in classification than some commonly used algorithms.