Volume 2, Issue 6, November 2016, Page: 62-71
A Hybrid Generic Algorithm for Dynamic Data Mining in Investment Decision Making
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
Received: Oct. 16, 2016;       Accepted: Nov. 8, 2016;       Published: Dec. 9, 2016
DOI: 10.11648/j.ijdst.20160206.12      View  2826      Downloads  78
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.
Keywords
Dynamic Data Mining, Investment Decision, Hybrid Genetic Algorithms, Risk Management
To cite this article
Kangzhi Yu, Yufang Li, Zhengying Cai, A Hybrid Generic Algorithm for Dynamic Data Mining in Investment Decision Making, International Journal on Data Science and Technology. Vol. 2, No. 6, 2016, pp. 62-71. doi: 10.11648/j.ijdst.20160206.12
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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