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Pacific Rim International Conference on Artificial Intelligence

PRICAI 1998: PRICAI’98: Topics in Artificial Intelligence pp 260–271Cite as

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

  • Application of Fuzzy Logic
  • Conference paper
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Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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Author information

Authors and Affiliations

  1. Graduate School of Management, Korea Advanced Institute of Science & Technology, Cheongryang, P.O. Box 201, 130-650, Seoul, Korea

    Myoung-Jong Kim & Ingoo Han

  2. School of Business Administration, Sung Kyun Kwan University, 110-745, Seoul, Korea

    Kunchang Lee

Authors
  1. Myoung-Jong Kim
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  2. Ingoo Han
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  3. Kunchang Lee
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Editor information

Hing-Yan Lee Hiroshi Motoda

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© 1998 Springer-Verlag Berlin Heidelberg

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Kim, MJ., Han, I., Lee, K. (1998). The integration of machine and human knowledge by fuzzy logic for the prediction of stock price index. In: Lee, HY., Motoda, H. (eds) PRICAI’98: Topics in Artificial Intelligence. PRICAI 1998. Lecture Notes in Computer Science, vol 1531. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095275

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  • DOI: https://doi.org/10.1007/BFb0095275

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65271-7

  • Online ISBN: 978-3-540-49461-4

  • eBook Packages: Springer Book Archive

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