Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 421-424.doi: 10.11896/jsjkx.191200132

• Big Data & Data Science • Previous Articles     Next Articles

Study on Electric Vehicle Price Prediction Based on PSO-SVM Multi-classification Method

LI Bao-sheng1, QIN Chuan-dong1,2   

  1. 1 School of Mathematics and Information Science,North Minzu University,Yinchuan 750021,China
    2 Ningxia Key Laboratory of Intelligent Information and Big Data Processing,Yinchuan 750021,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:LI Bao-sheng,born in 1996,postgradu-ate.His main research interests include big data analysis and machine learning.
    QIN Chuan-dong,born in 1976,Ph.D,associate professor.His main research interests include machine learning and intelligent information processing.
  • Supported by:
    This work was supported by the Ningxia Advanced Intelligent Perception Control Technology Innovation Team (NSFC61362033,NXJG2017003,NXYLXK2017B09).

Abstract: With the promotion of new energy vehicles,electric vehicles have gradually entered thousands of households.There are many factors that affect the price of electric vehicles.Twenty attributes that affect the price of electric vehicles are studied by principle component analysis.First of all,the data are preprocessed by Pearson correlation coefficient method and PCA algorithm to obtain more essential sample attributes.Then,the new data are studied by multi-classification supervised learning.Based on the SVM model,the particle swarm optimization algorithm is used to optimize the parameters of the support vector machine model,and the multi-classification research of electric vehicle is realized successfully.The experimental results show that the multi-classification SVM model has significant effect.

Key words: Electric vehicle, Multi-classification problem, Support vector machine, Particle swarm optimization algorithm

CLC Number: 

  • TP305
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