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A Multistrategy Conceptual Analysis of Economic Data

  • Kenneth A. Kaufman
  • Ryszard S. Michalski

Abstract

The goal of the multistrategy tool, INLEN, is to serve as an intelligent assistant for discovering knowledge in large databases. INLEN has been applied to, and is well-suited for the exploration of databases consisting of economic and demographic facts and statistics. Preliminary experiments on several data sets have focused on discerning and comparing various patterns in the status and development of countries in different regions of the world. These experiments have provided some interesting and often unexpected results, and serve as an example of one way in which such data can be explored. This paper describes in brief the INLEN methodology, presents examples of its learning and discovery operators, and demonstrates its application to economic domains.

Keywords

Labor Force Participation Population Growth Rate Compare International Development Economic Data Economic Domain 
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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References

  1. Baim, P.W., “The PROMISE Method for Selecting Most Relevant Attributes for Inductive Learning Systems,” Report No. UIUCDCS-F-82-898, Department of Computer Science, University of Illinois, Urbana IL. (1982).Google Scholar
  2. Bloedorn, E., J. Wnek, and R.S. Michalski, “Multistrategy Constructive Induction: AQ17-MCI.” Proceedings of the Second International Workshop on Multistrategy Learning, Harper’s Ferry, WV (1993):188–203.Google Scholar
  3. Central Intelligence Agency. 1993 World Factbook (1993).Google Scholar
  4. Kaufman, K. “Comparing International Development Patterns Using Multi-Operator Learning and Discovery Tools.” Proceedings of AAAI-94 Workshop on Knowledge Discovery in Databases, Seattle, WA (1994):431–440.Google Scholar
  5. Michalski, R.S. “Inferential Theory of Learning as a Conceptual Basis for Machine Learning.” Machine Learning 11 (1993):111–151.MathSciNetGoogle Scholar
  6. Michalski, R.S. and A.B. Baskin, “Integrating Multiple Knowledge Representations and Learning Capabilities in an Expert System: The ADVISE System.” Proceedings of the 8 th International Joint Conference on Artificial Intelligence, Karlsruhe, West Germany, (1983):256–258.Google Scholar
  7. Michalski, R.S., A.B. Baskin, and K.A. Spackman, “A Logic-based Approach to Conceptual Database Analysis”. Sixth Annual Symposium on Computer Applications in Medical Care (SCAMC-6), George Washington University Medical Center, Washington, DC (1982):792–796.Google Scholar
  8. Michalski, R.S., L. Kerschberg, K. Kaufman, and J. Ribeiro, “Mining for Knowledge in Databases: The INLEN Architecture, Initial Implementation and First Results,” Journal of Intelligent Information Systems: Integrating AI and Database Technologies 1, 1 (1992):85–113.Google Scholar
  9. Quinlan, J.R. “Probabilistic Decision Trees,” in Y. Kodratoff and R.S. Michalski (eds.), Machine Learning: An Artificial Intelligence Approach, Volume III, San Mateo, CA: Morgan Kaufmann (1990).Google Scholar
  10. Reinke, R.E. “Knowledge Acquisition and Refinement Tools for the ADVISE Meta-Expert System,” Master’s Thesis, Department of Computer Science, University of Illinois, Urbana, IL (1984).Google Scholar
  11. Ribeiro, J.S., K.A. Kaufman, and L. Kerschberg, “Knowledge Discovery From Multiple Databases,” First International Conference on Knowledge Discovery and Data Mining, Montreal PQ. (1995).Google Scholar
  12. Spackman, K.A., “QUIN: Integration of Inferential Operators within a Relational Database” ISG 83-13, UIUCDCS-F-83-917, M.S. Thesis, Department of Computer Science, University of Illinois, Urbana, IL (1983).Google Scholar
  13. Wnek, J., K. Kaufman, E. Bloedorn, and R.S. Michalski, “Selective Induction Learning System AQ15c: The Method and User’s Guide”. Reports of the Machine Learning and Inference Laboratory, MLI 95-4, Center for Machine Learning and Inference, George Mason University (1995).Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Kenneth A. Kaufman
    • 1
  • Ryszard S. Michalski
    • 2
  1. 1.Machine Learning and Inference LaboratoryGeorge Mason UniversityFairfaxUSA
  2. 2.GMU Departments of Computer Science and Systems Engineering and the Institute of Computer SciencePolish Academy of SciencesPoland

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