A Small Sample Data Prediction Method Based on Global Data Shuffling

Lai Jun; Liu Zhen-yu; Liu Sheng-hai

doi:10.12052/gdutxb.200124

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A Small Sample Data Prediction Method Based on Global Data Shuffling

更新时间：2026-05-27

- A Small Sample Data Prediction Method Based on Global Data Shuffling
- Vol. 38, Issue 3, Pages: 17-21(2021)
- 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：
- 基金信息：
- DOI：10.12052/gdutxb.200124
  CLC： TP183
- Online First：10 May 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
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

SEVGICAN S, TRRAN M, GOKARSLAN K, et al . Intelligent network data analytics function in 5G cellular networks using machine learning [J ] . Journal of Communications and Networks, 2020, 22(3): 269-280.

MADHURI C R, ANURADHA G, PUJITHA M V. House price prediction using regression techniques: a comparative study[C]//2019 International Conference on Smart Structures and Systems (ICSSS). Chennai: IEEE, 2019: 1-5.

JAHANDARI S, KALHOR A, ARAABI B N. Online forecasting of synchronous time series based on evolving linear models [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50(5): 1865-1876.

HASAN M M, SULTANA M I, SALMA U, et al . Investigation of influential factors towards predicting birth rate in Bangladesh[C ] //2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). Vellore: IEEE, 2020: 1-6.

XIE Z D, LIU X Q, WU J C, et al . A Study of passenger flow prediction based on IC card data [J ] . Journal of Guangdong University of Technology, 2018, 35(1): 16-22.

BEAN W T, STAFFORD R, BRASHARES J S. The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models [J]. Ecography, 2012, 35: 250-258.

ASIRET S, SUNBUL S O. Investigating test equating methods in small samples through various factors [J]. Educational Sciences: Theory Practice, 2016, 16(2): 647-668.

DABBAGHCHIAN S, AGHAGOLZADEH A, MOIN M S. Reducing the effects of small sample size in DCT domain for face recognition[C]//2008 International Symposium on Telecommunications. Tehran: IEEE, 2008: 634-638.

ZHANG H, YUAN H, LI P. Estimation method for extremely small sample accelerated degradation test data[C]//First International Conference on Reliability Systems Engineering (ICRSE). Beijing: IEEE, 2015: 21-23.

FURSOV V A, GAVRILOV A V, KOTOV A P. Prediction of estimates' accuracy for linear regression with a small sample size[C]//2018 41st International Conference on Telecommunications and Signal Processing (TSP). Athens: IEEE, 2018: 1-7.

ZHENG C, WANG N, CUI J. Hyperspectral Image Classification With Small Training Sample Size Using Superpixel-Guided Training Sample Enlargement [J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(10): 7307-7316.

TSAKIRIS M C, PENG L, CONCA A, et al . An Algebraic-Geometric Approach for Linear Regression Without Correspondences [J ] . IEEE Transactions on Information Theory, 2020, 66(8): 5130-5144.

GOODFELLOW I, BENGIO Y, COURVILLE A. Deep Learning[M]. Cambridge: MIT Press, 2016.

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