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A Weighted Voting Classifier Based on Differential Evolution

Author

Listed:
  • Yong Zhang
  • Hongrui Zhang
  • Jing Cai
  • Binbin Yang

Abstract

Ensemble learning is to employ multiple individual classifiers and combine their predictions, which could achieve better performance than a single classifier. Considering that different base classifier gives different contribution to the final classification result, this paper assigns greater weights to the classifiers with better performance and proposes a weighted voting approach based on differential evolution. After optimizing the weights of the base classifiers by differential evolution, the proposed method combines the results of each classifier according to the weighted voting combination rule. Experimental results show that the proposed method not only improves the classification accuracy, but also has a strong generalization ability and universality.

Suggested Citation

  • Yong Zhang & Hongrui Zhang & Jing Cai & Binbin Yang, 2014. "A Weighted Voting Classifier Based on Differential Evolution," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-6, May.
    • Handle: RePEc:hin:jnlaaa:376950
    DOI: 10.1155/2014/376950
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    Cited by:

    1. Sung-Mook Oh & Jin Park & Jinsun Yang & Young-Gyun Oh & Kyung-Woo Yi, 2023. "Smart classification method to detect irregular nozzle spray patterns inside carbon black reactor using ensemble transfer learning," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2729-2745, August.
    2. Liu, Qiang & Zhang, Hailin, 2022. "Reliability evaluation of weighted voting system based on D–S evidence theory," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    3. Liu, Qiang, 2021. "Reliability evaluation of two-stage evidence classification system considering preference and error," Reliability Engineering and System Safety, Elsevier, vol. 213(C).

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