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Effective learning model of user classification based on ensemble learning algorithms

  • Qunsheng Ruan
  • Qingfeng WuEmail author
  • Yingdong Wang
  • Xiling Liu
  • Fengyu Miao
Article
  • 49 Downloads

Abstract

Aiming to aid Electric-Power Industry to accurately understand users, hybrid learning model based ensemble learning algorithms for recognizing user to be sensitive to electric charge is proposed in this paper. On the basis of big data presented by CCF competition sponsor in China, with some excellent technology or algorithm such as JieBa, SFFS, etc., we extract many key features from data set and successfully draw a portrait for users who pay close attention to electric charge. Furthermore, machine learning algorithms and the strategy selection model related to them are investigated. The feasibility that hybrid learning model combining several ensemble learning algorithms can substantially improve classification accuracy are proved from theoretical level. Then the details of implementing hybrid learning model are described in the paper. Lastly, the hybrid learning model named Stacking is achieved, which yields better performance in contrast to the state-of-the-art competitors. The experimental results indicate that Stacking has both high precision and recall with 0.8 and 0.85 respectively. Furthermore the F1 score of Stacking evaluation is 0.823.

Keywords

User classification User portrait Machine learning algorithms Hybrid learning model 

Mathematics Subject Classification

68U04 

Notes

Acknowledgements

This work has partly been supported by the Key project of national key R&D project (No. 2017YFC17003303), National Nature Science Foundation of China (Nos. 61402387), Science and Technology Guiding Project of Fujian Province of China (Nos. 2015H0037, 2016H0035), the Natural Science Foundation of Fujian Province, China (Grant Nos. 2017J01773, 2018J01555), the Educational Middle and Youth Foundation of Fujian Province, China (Grant No. JAT160537), the research program of normal university (Grant Nos. 2016Z06, 2016Z03), The authors would like to appreciate the valuable comments and suggestions from the editors and reviewers.

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • Qunsheng Ruan
    • 1
  • Qingfeng Wu
    • 1
    Email author
  • Yingdong Wang
    • 1
  • Xiling Liu
    • 2
  • Fengyu Miao
    • 2
  1. 1.Software SchoolXiamen UniversityXiamenChina
  2. 2.School of Information, Mechanical and Electrical EnginerringNingde Normal UniversityNingdeChina

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