Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 270-274.doi: 10.11896/jsjkx.200700036

• Intelligent Computing • Previous Articles     Next Articles

Ensemble Learning Algorithm Based on Intuitionistic Fuzzy Sets

DAI Zong-ming, HU Kai, XIE Jie, GUO Ya   

  1. Key Laboratory of Advanced Process Control for Light Industry,Ministry of Education,Jiangnan University,Wuxi,Jiangsu 214122,China
    School of Internet of Things,Jiangnan University,Wuxi,Jiangsu 214122,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:DAI Zong-ming,born in 1995,postgra-duate.His main research interests include text classification and fuzzy decision.
    GUO Ya,born in 1977,Ph.D,professor,Ph.D supervisor.His main research interests include system modeling and control,and deep learning.
  • Supported by:
    National Natural Science Foundation of China(71904064),Open Research Fund of State Laboratory of Information Engineering in Surveying,the Mapping and Remote Sensing,Wuhan University(18I04),Natural Science Foundation of Jiangsu Province(BK20190580) and 111 Project and the Fundamental Research Funds for the Central Universities(JUSRP11922).

Abstract: In order to improve the classification accuracy and generalization ability of traditional machine learning algorithms,this paper proposes an ensemble learning algorithm based on intuitionistic fuzzy sets (IFS-EL).The algorithm constructs an intuitionistic fuzzy preference relation (IFPR) matrix according to the classification accuracy of the traditional classifier.The matrix is used to determine the weights of the classifiers and the multi-criteria group decision making (MCGDM) is used to determine the sample classification result.The experimental data uses 7 classification data sets in UCI,and the training set and test set are divided into 7:3.The classification results are compared with the current popular traditional classification algorithms and ensemble learning classification algorithms,SVM,LR,NB,Boosting,Bagging,the average accuracy of the algorithm in this paper is improved by 1.91%,3.89%,7.80%,3.66%,4.72%.The experimental results show that the IFS-EL can improve the classification accuracy and generalization ability.

Key words: Classification, Ensemble learning, Intuitionistic fuzzy sets, Multi-criteria group decision making

CLC Number: 

  • TP301.6
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