International Journal on Data Science and Technology

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A Hybrid Generic Algorithm for Dynamic Data Mining in Investment Decision Making

Received: 16 October 2016    Accepted: 08 November 2016    Published: 09 December 2016
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Abstract

To solve the risks and uncertainty problem in investment decision-making, a dynamic data mining architecture is introduced here. First, the investment decision-making process is examined and the involved risks are analyzed. Accordingly, dynamic data mining architecture is proposed here with the dynamic search ability of the generic algorithm. Second, a hybrid algorithm with dynamic learning ability is submitted to overcome the local minima problem prevalent in dynamic data mining. Whenever new data are generated, the data mining algorithm can dynamically collect the original input data without any reconstruction, to realize the dynamic update for investment decision-making. Last, an example is illustrated to verify the proposed model, and the solution provides us an effective model to improve the robustness of investment decision-making under risk environment.

DOI 10.11648/j.ijdst.20160206.12
Published in International Journal on Data Science and Technology (Volume 2, Issue 6, November 2016)
Page(s) 62-71
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Dynamic Data Mining, Investment Decision, Hybrid Genetic Algorithms, Risk Management

References
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Author Information
  • College of Computer and Information Technology, China Three Gorges University, Yichang, China

  • College of Economics and Management, China Three Gorges University, Yichang, China

  • College of Computer and Information Technology, China Three Gorges University, Yichang, China

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  • APA Style

    Kangzhi Yu, Yufang Li, Zhengying Cai. (2016). A Hybrid Generic Algorithm for Dynamic Data Mining in Investment Decision Making. International Journal on Data Science and Technology, 2(6), 62-71. https://doi.org/10.11648/j.ijdst.20160206.12

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    ACS Style

    Kangzhi Yu; Yufang Li; Zhengying Cai. A Hybrid Generic Algorithm for Dynamic Data Mining in Investment Decision Making. Int. J. Data Sci. Technol. 2016, 2(6), 62-71. doi: 10.11648/j.ijdst.20160206.12

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    AMA Style

    Kangzhi Yu, Yufang Li, Zhengying Cai. A Hybrid Generic Algorithm for Dynamic Data Mining in Investment Decision Making. Int J Data Sci Technol. 2016;2(6):62-71. doi: 10.11648/j.ijdst.20160206.12

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  • @article{10.11648/j.ijdst.20160206.12,
      author = {Kangzhi Yu and Yufang Li and Zhengying Cai},
      title = {A Hybrid Generic Algorithm for Dynamic Data Mining in Investment Decision Making},
      journal = {International Journal on Data Science and Technology},
      volume = {2},
      number = {6},
      pages = {62-71},
      doi = {10.11648/j.ijdst.20160206.12},
      url = {https://doi.org/10.11648/j.ijdst.20160206.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijdst.20160206.12},
      abstract = {To solve the risks and uncertainty problem in investment decision-making, a dynamic data mining architecture is introduced here. First, the investment decision-making process is examined and the involved risks are analyzed. Accordingly, dynamic data mining architecture is proposed here with the dynamic search ability of the generic algorithm. Second, a hybrid algorithm with dynamic learning ability is submitted to overcome the local minima problem prevalent in dynamic data mining. Whenever new data are generated, the data mining algorithm can dynamically collect the original input data without any reconstruction, to realize the dynamic update for investment decision-making. Last, an example is illustrated to verify the proposed model, and the solution provides us an effective model to improve the robustness of investment decision-making under risk environment.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - A Hybrid Generic Algorithm for Dynamic Data Mining in Investment Decision Making
    AU  - Kangzhi Yu
    AU  - Yufang Li
    AU  - Zhengying Cai
    Y1  - 2016/12/09
    PY  - 2016
    N1  - https://doi.org/10.11648/j.ijdst.20160206.12
    DO  - 10.11648/j.ijdst.20160206.12
    T2  - International Journal on Data Science and Technology
    JF  - International Journal on Data Science and Technology
    JO  - International Journal on Data Science and Technology
    SP  - 62
    EP  - 71
    PB  - Science Publishing Group
    SN  - 2472-2235
    UR  - https://doi.org/10.11648/j.ijdst.20160206.12
    AB  - To solve the risks and uncertainty problem in investment decision-making, a dynamic data mining architecture is introduced here. First, the investment decision-making process is examined and the involved risks are analyzed. Accordingly, dynamic data mining architecture is proposed here with the dynamic search ability of the generic algorithm. Second, a hybrid algorithm with dynamic learning ability is submitted to overcome the local minima problem prevalent in dynamic data mining. Whenever new data are generated, the data mining algorithm can dynamically collect the original input data without any reconstruction, to realize the dynamic update for investment decision-making. Last, an example is illustrated to verify the proposed model, and the solution provides us an effective model to improve the robustness of investment decision-making under risk environment.
    VL  - 2
    IS  - 6
    ER  - 

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