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

Kangzhi Yu; Yufang Li; Zhengying Cai

doi:doi:10.11648/j.ijdst.20160206.12

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

Kangzhi Yu, Yufang Li, Zhengying Cai

Published in International Journal on Data Science and Technology (Volume 2, Issue 6)

Received: 16 October 2016 Accepted: 8 November 2016 Published: 9 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.

Published in	International Journal on Data Science and Technology (Volume 2, Issue 6)
DOI	10.11648/j.ijdst.20160206.12
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), 2016. Published by Science Publishing Group

Keywords

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

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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://article.sciencepublishinggroup.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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Author Information

Kangzhi Yu

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

College of Economics and Management, China Three Gorges University, Yichang, China
Zhengying Cai

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

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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