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The integration of machine and human knowledge by fuzzy logic for the prediction of stock price index

  • Myoung-Jong Kim
  • Ingoo Han
  • Kunchang Lee
Application of Fuzzy Logic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1531)

Abstract

Integration of machine and human knowledge is more effective rather than a single kind of knowledge in solving unstructured problems. This paper proposes the knowledge integration of machine and human knowledge to achieve a better reasoning performance in the stock price index prediction problem. Causal model and the evaluation by experts generate the machine and human knowledge about the stock price index of next month, respectively. The machine and human knoledge are integrated by fuzzy logic-driven framework to generate the integrated knowledge. The conflicts among the integrated knowledge are solved by fuzzy rule base. The experimental results show that the propsed knowledge integrtion significantly improves the reasoning performance.

Keywords

Fuzzy Rule Human Knowledge Causal Model Fuzzy Membership Function Integrate Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Myoung-Jong Kim
    • 1
  • Ingoo Han
    • 1
  • Kunchang Lee
    • 2
  1. 1.Graduate School of ManagementKorea Advanced Institute of Science & TechnologySeoulKorea
  2. 2.School of Business AdministrationSung Kyun Kwan UniversitySeoulKorea

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