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Application of Differential Evolution Algorithm Based on Mixed Penalty Function Screening Criterion in Imbalanced Data Integration Classification

Author

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  • Yuelin Gao

    (Ningxia Province Key Laboratory of Intelligent Information and Data Processing, North Minzu University, Yinchuan 750021, China)

  • Kaiguang Wang

    (Ningxia Province Key Laboratory of Intelligent Information and Data Processing, North Minzu University, Yinchuan 750021, China)

  • Chenyang Gao

    (School of Cyber Engineering, Xidian University, Xi’an 710071, China)

  • Yulong Shen

    (School of Cyber Engineering, Xidian University, Xi’an 710071, China)

  • Teng Li

    (School of Cyber Engineering, Xidian University, Xi’an 710071, China)

Abstract

There are some processing problems of imbalanced data such as imbalanced data sets being difficult to integrate efficiently. This paper proposes and constructs a mixed penalty function data integration screening criterion, and proposes Differential Evolution Integration Algorithm Based on Mixed Penalty Function Screening Criteria (DE-MPFSC algorithm). In addition, the theoretical validity and the convergence of the DE-MPFSC algorithm are analyzed and proven by establishing the Markov sequence and Markov evolution process model of the DE-MPFSC algorithm. In this paper, the entanglement degree and enanglement degree error are introduced to analyze the DE-MPFSC algorithm. Finally, the effectiveness and stability of the DE-MPFSC algorithm are verified by UCI machine learning datasets. The test results show that the DE-MPFSC algorithm can effectively improve the effectiveness and application of imbalanced data classification and integration, improve the internal classification of imbalanced data and improve the efficiency of data integration.

Suggested Citation

  • Yuelin Gao & Kaiguang Wang & Chenyang Gao & Yulong Shen & Teng Li, 2019. "Application of Differential Evolution Algorithm Based on Mixed Penalty Function Screening Criterion in Imbalanced Data Integration Classification," Mathematics, MDPI, vol. 7(12), pages 1-36, December.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:12:p:1237-:d:297761
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    References listed on IDEAS

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    1. Mikalef, Patrick & Boura, Maria & Lekakos, George & Krogstie, John, 2019. "Big data analytics and firm performance: Findings from a mixed-method approach," Journal of Business Research, Elsevier, vol. 98(C), pages 261-276.
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    Cited by:

    1. Dalue Lin & Haogan Huang & Xiaoyan Li & Yuejiao Gong, 2022. "Empirical Study of Data-Driven Evolutionary Algorithms in Noisy Environments," Mathematics, MDPI, vol. 10(6), pages 1-26, March.

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