BGRF: A broad granular random forest algorithm

Author:

Fu Xingyu1,Chen Yingyue2,Yan Jingru2,Chen Yumin1,Xu Feng3

Affiliation:

1. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

2. School of Economics and Management, Xiamen University of Technology, Xiamen, China

3. Beijing Srit Software Technology Co., Ltd., Beijing, China

Abstract

 The random forest is a combined classification method belonging to ensemble learning. The random forest is also an important machine learning algorithm. The random forest is universally applicable to most data sets. However, the random forest is difficult to deal with uncertain data, resulting in poor classification results. To overcome these shortcomings, a broad granular random forest algorithm is proposed by studying the theory of granular computing and the idea of breadth. First, we granulate the breadth of the relationship between the features of the data sets samples and then form a broad granular vector. In addition, the operation rules of the granular vector are defined, and the granular decision tree model is proposed. Finally, the multiple granular decision tree voting method is adopted to obtain the result of the granular random forest. Some experiments are carried out on several UCI data sets, and the results show that the classification performance of the broad granular random forest algorithm is better than that of the traditional random forest algorithm.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference35 articles.

1. Application of OPTICS and ensemblelearning for Database Intrusion Detection;Subudhi;Journal of King SaudUniversity-Computer and Information Sciences,2022

2. learning-based dynamic line rating forecasting undercyberattacks;Ahmadi;IEEE Transactions on Power Delivery,2022

3. Ensemble machinelearning-based affective computing for emotion recognition usingdual-decomposed EEG signals;Kamble;IEEE Sensors Journal,2022

4. Image denoising via sequentialensemble learning,;Yang;IEEE Transactions on Image Processing,2020

5. Prediction byfuzzy clustering and KNN on validation data with parallel ensembleof interpretable TSK fuzzy classifiers;Zhang;IEEE Transactions onSystems Man Cybernetics-Systems,2022

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3