IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v7y2019i12p1237-d297761.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/7/12/1237/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/7/12/1237/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mariani, Marcello M. & Fosso Wamba, Samuel, 2020. "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies," Journal of Business Research, Elsevier, vol. 121(C), pages 338-352.
    2. Lutfi, Abdalwali & Alrawad, Mahmaod & Alsyouf, Adi & Almaiah, Mohammed Amin & Al-Khasawneh, Ahmad & Al-Khasawneh, Akif Lutfi & Alshira'h, Ahmad Farhan & Alshirah, Malek Hamed & Saad, Mohamed & Ibrahim, 2023. "Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    3. Luther Yuong Qai Chong & Thien Sang Lim, 2022. "Pull and Push Factors of Data Analytics Adoption and Its Mediating Role on Operational Performance," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    4. Raed A.I. Abueed & Mehmet Aga, 2019. "Sustainable Knowledge Creation and Corporate Outcomes: Does Corporate Data Governance Matter?," Sustainability, MDPI, vol. 11(20), pages 1-15, October.
    5. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    6. Andrea Chiarini, 2021. "Industry 4.0 technologies in the manufacturing sector: Are we sure they are all relevant for environmental performance?," Business Strategy and the Environment, Wiley Blackwell, vol. 30(7), pages 3194-3207, November.
    7. Jingmei Gao & Zahid Sarwar, 2024. "How do firms create business value and dynamic capabilities by leveraging big data analytics management capability?," Information Technology and Management, Springer, vol. 25(3), pages 283-304, September.
    8. Changchun Zhu & Jianguo Du & Fakhar Shahzad & Muhammad Umair Wattoo, 2022. "Environment Sustainability Is a Corporate Social Responsibility: Measuring the Nexus between Sustainable Supply Chain Management, Big Data Analytics Capabilities, and Organizational Performance," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    9. Jiang, Kangqi & Du, Xinyi & Chen, Zhongfei, 2022. "Firms' digitalization and stock price crash risk," International Review of Financial Analysis, Elsevier, vol. 82(C).
    10. Shah, Tushar R., 2022. "Can big data analytics help organisations achieve sustainable competitive advantage? A developmental enquiry," Technology in Society, Elsevier, vol. 68(C).
    11. Steven März & Anja Bierwirth & Ralf Schüle, 2020. "Mixed-Method Research to Foster Energy Efficiency Investments by Small Private Landlords in Germany," Sustainability, MDPI, vol. 12(5), pages 1-18, February.
    12. Souiden, Nizar & Amara, Nabil & Chaouali, Walid, 2020. "Optimal image mix cues and their impacts on consumers’ purchase intention," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    13. Maria Petrescu & Anjala S. Krishen, 2019. "Strength in diversity: methods and analytics," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(4), pages 203-204, December.
    14. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    15. Maya Vachkova & Arsalan Ghouri & Haidy Ashour & Normalisa Binti Md Isa & Gregory Barnes, 2023. "Big data and predictive analytics and Malaysian micro-, small and medium businesses," SN Business & Economics, Springer, vol. 3(8), pages 1-28, August.
    16. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    17. Xue, Fujing & Li, Xiaoyu & Zhang, Ting & Hu, Nan, 2021. "Stock market reactions to the COVID-19 pandemic: The moderating role of corporate big data strategies based on Word2Vec," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    18. Tsionas, Mike & Patel, Pankaj C. & Guedes, Maria João, 2022. "Endogenous efficiency of the dynamic profit maximization in the intertemporal production models of venture behavior," International Journal of Production Economics, Elsevier, vol. 246(C).
    19. Han Bu & Zhou Xun & Sha Cai, 2024. "Big data and inter-firm wage disparities: theory and evidence from China," Economic Change and Restructuring, Springer, vol. 57(4), pages 1-36, August.
    20. Kalaitzi, Dimitra & Tsolakis, Naoum, 2022. "Supply chain analytics adoption: Determinants and impacts on organisational performance and competitive advantage," International Journal of Production Economics, Elsevier, vol. 248(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:7:y:2019:i:12:p:1237-:d:297761. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.