A Method for Unbalanced Big Data Classification Based on Optimization Random Forest

MA Hai-rong; CHENG Xin-wen

Chinese

您当前的位置：

首页 >

文章列表页 >

A Method for Unbalanced Big Data Classification Based on Optimization Random Forest

更新时间：2026-09-04

- A Method for Unbalanced Big Data Classification Based on Optimization Random Forest
- Vol. 35, Issue 11, Pages: 28-32(2018)
- 作者机构：
  
  1. 湖北省农业科学院农业经济技术研究所,湖北,武汉,430064
  2. 中国地质大学(武汉)信息工程学院,湖北,武汉,430074
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2018
- 稿件说明：
移动端阅览
马海荣, 程新文. 一种处理非平衡数据集的优化随机森林分类方法[J]. 微电子学与计算机, 2018, 35(11): 28-32.

MA Hai-rong, CHENG Xin-wen. A Method for Unbalanced Big Data Classification Based on Optimization Random Forest[J]. 2018, 35(11): 28-32.
马海荣, 程新文. 一种处理非平衡数据集的优化随机森林分类方法[J]. 微电子学与计算机, 2018, 35(11): 28-32. DOI：

MA Hai-rong, CHENG Xin-wen. A Method for Unbalanced Big Data Classification Based on Optimization Random Forest[J]. 2018, 35(11): 28-32. DOI：

摘要

利用传统随机森林(random forest

RF)模型进行分类时存在分类精度受不平衡样本集的影响

以及投票平局现象会导致算法停滞等问题.本文对RF模型进行了优化改进

首先随机抽取等量的少数类与多数类样本构建训练样本集进行RF建模

然后根据投票熵与基于样本特征参数的广义欧几里得距离逐步添加具有最大投票熵的样本到训练样本集

解决传统RF模型随机抽取样本时训练样本集中包含不同类别样本数不平衡问题.对于分类过程中可能出现投票结果的平局现象

利用测试样本与邻近训练样本的广义欧几里得距离决定其分类结果

以消除投票平局现象造成的停滞问题.实验结果表明

本文优化RF模型对于非平衡数据集的分类可以取得较好的分类结果.

Abstract

When utilized traditional random forest (RF) model for classification

there were following problems exist:for example

the classification accuracy was affected by the unbalanced sample set

equality votes of each class would lead to algorithms stalling. We improved the traditional RF model

first of all

we randomly selected the same number of samples from minority class and majority class to build training sample set for RF modeling. Then

according to the voting entropy and the generalized Euclidean distance based on the sample characteristic parameters gradually add the sample with maximum voting entropy to the training sample set. This could solve the problem that in traditional RF model training samples randomly selected contained too few minority class samples. In the classification process when the voting draw occurs

we utilized the generalized Euclidean distance between the test samples and the adjacent training samples to determine the classification result

this would eliminate the stagnation caused by the equality votes of each class. The experimental results show that the optimized RF model in this paper could achieve better classification results for unbalanced data sets.

关键词

Keywords

references

Behling R, Roessner S, Kaufmann H, et al. Automated spatiotemporal landslide mapping over large areas using rapid eye time series data[J]. Remote Sensing, 2014, 6(9):8026-8055. DOI: 8026

Weiss G M.Mining with rarity:a unifying framework[J].Acm Sigkdd Explorations Newsletter, 2004, 6(1):7-19. DOI: 7

刘亚东, 崔日鲜.基于可见光光谱和随机森林算法的冬小麦冠层图像分割[J].光谱学与光谱分析, 2015, 35(12):3480-3484. DOI: 3480

秦静, 钱雪忠, 王卫涛, 等.一种处理不平衡大数据的并行随机森林算法[J].微电子学与计算机, 2017, 34(4):22-27. DOI: 22

Stumpf A, Kerle N. Combining random forests and object-oriented analysis for landslide mapping from very high resolution imagery[J]. Procedia Environmental Sciences, 2011, 3(1):123-129. DOI: 123

马越, 贺光辉.一种改进的基于随机森林的快速人眼定位方法[J].微电子学与计算机, 2016, 33(7):1-4. DOI: 1

焦盛岚, 杨炳儒, 翟云, 等.一种用于非平衡数据分类的集成学习模型[J].计算机工程与应用, 2012, 48(29):119-123. DOI: 119

Martha T R, Kerle N, Westen C J V, et al.Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories[J].Isprs Journal of Photogrammetry % Remote Sensing, 2012, 67(2):105-119. DOI: 105

曹正凤.随机森林算法优化研究[D].北京: 首都经济贸易大学, 2014.

Breiman L.Random forests[J].Machine Learning, 2001, 45(1):5-32. DOI: 5

Ho T K.The random subspace method for constructing decision forests[J].IEEE Transactions on Pattern Analysis % Machine Intelligence, 1998, 20(8):832-844. DOI: 832

赵悦, 穆志纯.基于委员会投票选择方法的主动学习的研究[J].太原理工大学学报, 2006, 37(4):469-472. DOI: 469

尹留志.关于非平衡数据特征问题的研究[D].合肥: 中国科学技术大学, 2014.

陈荣鑫.R软件的数据挖掘应用[J].重庆工商大学学报(自然科学版), 2011, 28(6):602-607. DOI: 602

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A review of large model development: technological evolution from BERT to Deepseek

Monte carlo analysis of V-layout trench SiC neutron detector

A feature-oriented context understanding and slice analysis network for tuberculosis detection

Aerial small object detection algorithm based on improved RT-DETR

High gain stability high temperature operational amplifier based on reverse body bias

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰