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
[1] LIN Q Y,QIU G Y,ZENG H,et al.Research on Price subsidy and Sustainability of Pure Electric vehicles in China based on Learning Curve[J].Management Modernization,2019,39(3):39-43.
[2] CORTES C,VAPNIK V.Support-Vector Networks[J].Ma-chine Learning,1995,20:273-297.
[3] 邓乃扬,田英杰.数据挖掘中的新方法-支持向量机[M].北京:科学出版社,2004.
[4] 杨晓峰,郝志峰.支持向量机的算法设计与分析[M].北京:科学出版社,2013.
[5] WETSON J,WATKINS C.Support vector machines for multiclass pattern recognition[R].Proceedings of the 7th European Symposium on Artificial Neural Networks.1999.
[6] TOMAR D,AGARWAL S.A comparison on multi-class classification methods based on least squares twin support vector machine[J].Knowledge-Based Systems.2015,81(Jun.):131-147.
[7] ZHENG C H,JIAO L H.Automatic parameters selection for SVM based on GA[C]//Proceedings of 5th World Congress on Intelligent Control and Automation.Piscataway:IEEE Press,2004:1869-1872.
[8] ZHANG X L,CHEN X F,HE Z J.An ACO-based algorithm for parameter optimization of support vector machines[J].Expert Systems with Applications,2010,37(9):6618-6628.
[9] RANAEE V,EBRAHIMZADEH A,GHADERI R.Application of the PSO-SVM model for recognition of control chart patterns [J].ISA Transactions.2010,49(4):577-586.
[10] TREVORHASTIE,TIBSHIRANI R,FRIEDMAN J M.Theelements of statistical learning[M].12th Springer series in statistic,2017.
[11] ARDJANI F,SADOUNI K.Optimization of SVM Multiclass by Particle Swarm (PSO-SVM)[J].IJMECS,2010,2(2):32-38.
[12] JU X C,TIAN Y J,LIU D L,et al.Nonparallel Hyperplanes Support Vector Machine for Multi-class Classification[J].Procedia Computer Science,2015,51:1574-1582.
[13] KAYA D.Optimization of SVM Parameters with Hybrid CS-PSO Algorithms for Parkinson's Disease in LabVIEW Environment[J].Parkinson's Disease,2019,5:1-9.
[14] SHI Y,EBERHART R C.A modified particle swarm optimizer,[C]//1998 IEEE International Conference on Evolutionary Computation Proceedings.IEEE World Congresson Computational Intelligence (Cat.No.98TH8360).Anch-orage,AK,USA,1998:69-73.
[15] SHI Y,EBERHART R C.Empirical study of particle swarm optimization[C]//Proceedings of the 1999 Congress onEvolutiona-ry Computation-CEC99 (Cat.No.99TH8406).Washington,DC,USA,1999:1945-1950.
[16] LIU Y,ZHENG Y F.One-against-all multi-class SVM classification using reliability measures[C]//Proceedings.2005 IEEE International Joint Conference on Neural Networks.Montreal,2005:849-854.
[17] CRAMMER K,SINGER Y.On the Algo-rithmic Implementation of Multiclass Kernel-based Vector Machines[J].Journal of Machine Learning Research,2001(2):265-292.
[1] CAO Su-e, YANG Ze-min. Prediction of Wireless Network Traffic Based on Clustering Analysis and Optimized Support Vector Machine [J]. Computer Science, 2020, 47(8): 319-322.
[2] SONG Yan, HU Rong-hua, GUO Fu-min, YUAN Xin-liang and XIONG Rui-yang. Improved SVM+BP Algorithm for Muscle Force Prediction Based on sEMG [J]. Computer Science, 2020, 47(6A): 75-78.
[3] FANG Meng-lin, TANG Wen-bing, HUANG Hong-yun and DING Zuo-hua. Wall-following Navigation of Mobile Robot Based on Fuzzy-based Information Decomposition and Control Rules [J]. Computer Science, 2020, 47(6A): 79-83.
[4] XU Xiang-yan and HOU Rui-huan. Medium and Long-term Population Prediction Based on GM(1,1)-SVM Combination Model [J]. Computer Science, 2020, 47(6A): 485-487.
[5] PAN Heng, LI Jing feng, MA Jun hu. Role Dynamic Adjustment Algorithm for Resisting Insider Threat [J]. Computer Science, 2020, 47(5): 313-318.
[6] YANG Li, LI Xin-yu, SHI Huai-feng, PAN Cheng-sheng. Task Intelligent Identification Method for Spatial Information Network [J]. Computer Science, 2020, 47(4): 262-269.
[7] GAO Nan,LI Li-juan,Wei-william LEE,ZHU Jian-ming. Keywords Extraction Method Based on Semantic Feature Fusion [J]. Computer Science, 2020, 47(3): 110-115.
[8] WU Yu-kun,XIAO Jie,Wei William LEE,LOU Ji-lin. Support Vector Machine Model Based on Grey Wolf Optimization Fused Asymptotic [J]. Computer Science, 2020, 47(2): 37-43.
[9] ZHU Xiao-ling, LI Kun, ZHANG Chang-sheng, DU Fu-xin. Elevator Boot Fault Diagnosis Method Based on Gabor Wavelet Transform and Multi-coreSupport Vector Machine [J]. Computer Science, 2020, 47(12): 258-261.
[10] ZHAO Rui-jie, SHI Yong, ZHANG Han, LONG Jun, XUE Zhi. Webshell File Detection Method Based on TF-IDF [J]. Computer Science, 2020, 47(11A): 363-367.
[11] ZHOU Yu, REN Qin-chai, NIU Hui-bin. Research on Training Sample Data Selection Methods [J]. Computer Science, 2020, 47(11A): 402-408.
[12] YAO Mu-yan, TAO Dan. Implicit Authentication Mechanism of Pattern Unlock Based on Over-sampling and One-class Classification for Smartphones [J]. Computer Science, 2020, 47(11): 19-24.
[13] ZHOU Xin-yue, QIAN Li-ping, HUANG Yu-pin, WU Yuan. Optimization Method of Electric Vehicles Charging Scheduling Based on Ant Colony [J]. Computer Science, 2020, 47(11): 280-285.
[14] TIAN Chun-yuan, YU Jiang, CHANG Jun, WANG Yan-shun. NWI:CSI Based Non-line-of-sight Signal Recognition Method [J]. Computer Science, 2020, 47(11): 327-332.
[15] JIN Yao,XU Li-ya,LV Hui-lin,GU Su-hang. Stacked Support Vector Machine Based on Attacks on Labels of Data Samples [J]. Computer Science, 2020, 47(1): 110-116.
Full text



[1] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[2] WU Shu, ZHOU An-min and ZUO Zheng. PDiOS:Private API Call Detection in iOS Applications[J]. Computer Science, 2018, 45(4): 163 -168 .
[3] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[4] ZHANG Jing and ZHU Guo-bin. Hot Topic Discovery Research of Stack Overflow Programming Website Based on CBOW-LDA Topic Model[J]. Computer Science, 2018, 45(4): 208 -214 .
[5] DING Shu-yang, LI Bing and SHI Hong-bo. Study on Flexible Job-shop Scheduling Problem Based on Improved Discrete Particle Swarm Optimization Algorithm[J]. Computer Science, 2018, 45(4): 233 -239 .
[6] WEI Qin-shuang, WU You-xi, LIU Jing-yu and ZHU Huai-zhong. Distinguishing Sequence Patterns Mining Based on Density and Gap Constraints[J]. Computer Science, 2018, 45(4): 252 -256 .
[7] CUI Jian-jing, LONG Jun, MIN Er-xue, YU Yang and YIN Jian-ping. Survey on Application of Homomorphic Encryption in Encrypted Machine Learning[J]. Computer Science, 2018, 45(4): 46 -52 .
[8] ZHU Jin-bin, WU Ji-gang and SUI Xiu-feng. Edge Cloud Clustering Algorithm Based on Maximal Clique[J]. Computer Science, 2018, 45(4): 60 -65 .
[9] LI Hui, ZHOU Lin and XIN Wen-bo. Optimization of Networked Air-defense Operational Formation Structure Based on Bilevel Programming[J]. Computer Science, 2018, 45(4): 266 -272 .
[10] LI Jian-hong, WU Ya-rong and LV Ju-jian. Online Single Image Super-resolution Algorithm Based on Group Sparse Representation[J]. Computer Science, 2018, 45(4): 312 -318 .