@inproceedings{3f6a1c3818c343a29d2d4d4d7a9412c2,
title = "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.",
keywords = "dichotomy rule fusion, information gain, random forest, recursive feature elimination",
author = "Yueyue Xiao and Wei Huang and Jinsong Wang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 10th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2020 ; Conference date: 17-07-2020 Through 19-07-2020",
year = "2020",
month = jul,
doi = "10.1109/ICEIEC49280.2020.9152236",
language = "English",
series = "ICEIEC 2020 - Proceedings of 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "182--185",
editor = "Wenzheng Li and Xuefei Zhang",
booktitle = "ICEIEC 2020 - Proceedings of 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication",
address = "United States",
}