基于全局数据混洗的小样本数据预测方法

赖峻; 刘震宇; 刘圣海

doi:10.12052/gdutxb.200124

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基于全局数据混洗的小样本数据预测方法

更新时间：2026-05-27

- 基于全局数据混洗的小样本数据预测方法
- A Small Sample Data Prediction Method Based on Global Data Shuffling
- 广东工业大学学报 2021年38卷第3期页码：17-21
- 作者机构：
  
  广东工业大学信息工程学院,广东,广州,510006
- 作者简介：
  
  赖峻(1979–)，男，讲师，博士，主要研究方向为深度学习、网络流量处理、视频处理等，E-mail：laijun@gdut.edu.cn
- 基金信息：
  
  广州市科技计划资助项目(201907010003)
- DOI：10.12052/gdutxb.200124
  中图分类号： TP183
- 网络首发：2021-05-10，
  
  纸质出版：2021
- 稿件说明：
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赖峻, 刘震宇, 刘圣海. 基于全局数据混洗的小样本数据预测方法[J]. 广东工业大学学报, 2021, 38(3): 17-21.

Lai Jun, Liu Zhen-yu, Liu Sheng-hai. A Small Sample Data Prediction Method Based on Global Data Shuffling[J]. 2021, 38(3): 17-21.
赖峻, 刘震宇, 刘圣海. 基于全局数据混洗的小样本数据预测方法[J]. 广东工业大学学报, 2021, 38(3): 17-21. DOI： 10.12052/gdutxb.200124.

Lai Jun, Liu Zhen-yu, Liu Sheng-hai. A Small Sample Data Prediction Method Based on Global Data Shuffling[J]. 2021, 38(3): 17-21. DOI： 10.12052/gdutxb.200124.

摘要

以广州车牌竞拍价格数据集为数据来源

采用线性回归并结合

折交叉验证

研究小样本数据集的预测方法。为解决小样本局部特异性数据导致的验证误差增大的问题

提出验证之前先对数据进行全局混洗的策略。最后通过实验验证了此策略可以明显降低验证误差

以此为基础

通过多组实验验证

确定了合适的参数

结果表明最终预测值的总平均正确率达到了95%。

Abstract

Based on the Guangzhou license plate auction price data set

linear regression combined with

-fold cross-validation is used to study the prediction method of a small sample data set. In order to solve the problem of increased verification errors caused by local specific data in a small sample set

a strategy to shuffle the data globally before verification is proposed. Finally

it is verified through experiments that this strategy can significantly reduce the verification error. Based on this

through multiple sets of experimental verification

the appropriate parameters are determined

and the results show that the total average correct rate of the final predicted value has reached 95%.

关键词

Keywords

references

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