An Improved Random Forest Algorithm Based on Attribute Compatibility
Yu Liu, Lu Liu, Yin Gao, Yang Liu · 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · 2019
ID3 algorithm and C4.5 algorithm of decision tree are commonly used in the base classifier in random forest. In order to solve the problem that the basic classifier algorithm is biased to select multi-valued attributes and contains a lot of logarithmic operations, the attribute compatibility of rough set is introduced in this paper. Rough set is a mathematical tool for dealing with uncertain information. In this paper, the expression of information gain or information gain rate in the base classifier is reconstructed with compatibility, and the attribute with the highest compatibility is selected as the segmentation attribute to segment the data set. Finally, three UCI data sets are used to verify the accuracy of the algorithm. The experimental results show that the proposed algorithm has a higher accuracy than the traditional random forest algorithm.