Journal of Guangdong University of Technology ›› 2021, Vol. 38 ›› Issue (03): 17-21.doi: 10.12052/gdutxb.200124

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A Small Sample Data Prediction Method Based on Global Data Shuffling

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

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2020-09-22 Online:2021-05-10 Published:2021-03-12

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Abstract: Based on the Guangzhou license plate auction price data set, linear regression combined with k-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%.

Key words: linear regression, k-fold cross-validation, stochastic gradient descent, data shuffling, deep learning

CLC Number: 

  • TP183
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