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International Symposium on Methodologies for Intelligent Systems

ISMIS 1996: Foundations of Intelligent Systems pp 108–117Cite as

The AQ17-DCI system for data-driven constructive induction and its application to the analysis of world economics

  • Communications Session 1B Learning and Discovery Systems
  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1079))

Abstract

Constructive induction divides the problem of learning an inductive hypothesis into two intertwined searches: one-for the “best” representation space, and two-for the “best” hypothesis in that space. In datadriven constructive induction (DCI), a learning system searches for a better representation space by analyzing the input examples (data). The presented datadriven constructive induction method combines an AQ-type learning algorithm with two classes of representation space improvement operators: constructors, and destructors. The implemented system, AQ17-DCI, has been experimentally applied to a GNP prediction problem using a World Bank database. The results show that decision rules learned by AQ17-DCI outperformed the rules learned in the original representation space both in predictive accuracy and rule simplicity.

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References

  1. Baim, P.W., “The PROMISE Method for Selecting the Most Relevant Attributes for Inductive Learning Systems”, Rep. No. UIUCDCS-F-82-898, Dept. of Computer Science, University of Illinois-Urbana Champaign, IL, 1982.

    Google Scholar 

  2. Bloedorn, E. and Michalski, R.S. “Data-Driven Constructive Induction in AQ17-PRE: A Method and Experiments”, Proceedings of the Third International Conference on Tools for AI, November 1991a.

    Google Scholar 

  3. Bloedorn, E. and Michalski, R.S., “Constructive Induction from Data in AQ17-DCI: Further Experiments,” Reports of the Machine Learning and Inference Laboratory, MLI 91-12, School of Information Technology and Engineering, George Mason University, Fairfax, VA, December 1991b.

    Google Scholar 

  4. Bloedorn, E., Michalski, R., and Wnek, J., “Multistrategy Constructive Induction,” Proceedings of the Second International Workshop on Multistrategy Learning,” Harpers Ferry, WV, May 26–29, 1993.

    Google Scholar 

  5. Bloedorn, E., Michalski, R.S., and Wnek, J., “Matching Methods with Problems: A Comparative Analysis of Constructive Induction Approaches”, Reports of the Machine Learning and Inference Laboratory, MLI 94-2, George Mason University, Fairfax, VA, 1994.

    Google Scholar 

  6. Bloedorn, E. and Kaufman, K., “Data-Driven Constructive Induction in INLEN”, Reports of the Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA, 1996 (to appear).

    Google Scholar 

  7. Dougherty, J., Kohavi, R., and Sahami, M., “Supervised and Unsupervised Discretization of Continuous Features”, Proceedings of the Twelfth International Conference on Machine Learning, pp. 194–201, June 1995.

    Google Scholar 

  8. Fulton, T., Kasif, S., and Salzberg S., “Efficient Algorithms for Finding Multi-way Splits for Decision Trees”, Proceedings of the Twelfth International Conference on Machine Learning, pp. 244–251., June 1995.

    Google Scholar 

  9. Greene, G.H., “Quantitative Discovery: Using Dependencies to Discover Non-Linear Terms”, M.S. Thesis, University of Illinois at Urbana-Champaign, 1988.

    Google Scholar 

  10. Gryzmala-Busse, J.W., “LERS — A System for Learning from Examples based on Rough Sets” in Slowinski, R., Ed. Intelligent Decision Support Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, pp. 3–18. 1992.

    Google Scholar 

  11. Jensen, G., “SYM-1: A Program that Detects Symmetry of Variable-Valued Logic Functions”, Report UIUCDCS-R-75-729, Department of Computer Science, University of Illinois at Urbana-Champaign, 1975.

    Google Scholar 

  12. Kaufman, K., “Comparing International Development Patterns Using Multi-Operator Learning and Discovery Tools”, Proceedings of the AAAI-94 Workshop on Knowledge Discovery in Databases, Seattle, WA, pp. 431–440. 1994.

    Google Scholar 

  13. Kerber, R., “ChiMerge: Discretization of Numeric Attributes”, Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 123–128, San Jose, CA, 1992.

    Google Scholar 

  14. Langley, P., “Rediscovering Physics with Bacon 3,” Fifth International Joint Conference on Artificial Intelligence, pp. 505–507, Cambridge, MA:, 1977.

    Google Scholar 

  15. Lenat, Douglas, “Learning from Observation and Discovery”, in Machine Learning: An Artificial Intelligence Approach, Vol. I, R.S. Michalski, J.G. Carbonell and T.M. Mitchell (Eds.). Palo Alto, CA: Morgan Kaufmann (reprint), 1983

    Google Scholar 

  16. Matheus, C. J. and Rendell, L.A., “Constructive Induction on Decision Trees”, In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pp. 645–650, 1989.

    Google Scholar 

  17. Michalski, R.S., “Recognition of Total or Partial Symmetry in a Completely or Incompletely Specified Switching Function,” Proceedings of the IV Congress of the International Federation on Automatic Control (IFAC), Vol. 27 (Finite Automata and Switching Systems), pp. 109–129, Warsaw, June 16–21, 1969.

    Google Scholar 

  18. Michalski, R.S., “On the Quasi-Minimal Solution of the Covering Problem” Proceedings of the V International Symposium on Information Processing (FCIP 69), Vol. A3 (Switching Circuits), Bled, Yugoslavia, pp. 125–128, 1969.

    Google Scholar 

  19. Michalski, R.S. and McCormick, B.H., “Interval Generalization of Switching Theory.” Report No. 442, Dept. of Computer Science, University of Illinois, Urbana. 1971.

    Google Scholar 

  20. Michalski, R.S., “Variable-Valued Logic: System VL1, Proceedings of the 1974 International Symposium on Multiple-Valued Logic, pp. 323–346. West Virginia University, Morgantown, 1974.

    Google Scholar 

  21. Michalski, R.S. and Larson, J.B., “Selection of Most Representative Training Examples and Incremental Generation of VL1 Hypotheses: the underlying methodology and the description of programs ESEL and AQ11, “ Report No. 867, Dept of Computer Science, University of Illinois, Urbana, 1978.

    Google Scholar 

  22. Michalski, R.S., “Pattern Recognition as Rule-Guided Inductive Inference,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 2, No. 4, pp. 349–361, 1980.

    Google Scholar 

  23. Michalski, R.S., “A Theory and Methodology of Inductive Learning: Developing Foundations for Multistrategy Learning,” in Machine Learning: An Artificial Intelligence Approach, Vol. I, R.S. Michalski, J.G. Carbonell and T.M. Mitchell (Eds.), Palo Alto, CA: Morgan Kaufmann (reprint), 1983.

    Google Scholar 

  24. Michalski, R.S., “Inferential Theory of Learning,” in Machine Learning: An Multistrategy Approach, Vol. IV, R.S. Michalski, and G. Tecuci (Eds.), Palo Alto, CA: Morgan Kaufmann, 1994.

    Google Scholar 

  25. Muggleton, S., “Duce, an Oracle-Based Approach to Constructive Induction”, Proceedings of IJCAI-87, pp. 287–292, Morgan Kaufman, Milan, Italy, 1987.

    Google Scholar 

  26. Pagallo, G., and Haussler, D., “Boolean Feature Discovery in Empirical Learning”, Machine Learning, vol. 5, pp. 71–99, 1990.

    Google Scholar 

  27. Pawlak, Z. “Rough Sets and their Applications”, Workshop on Mathematics and AI, Schloss Reisburg, W. Germany. Vol II. pp. 543–572. 1988.

    Google Scholar 

  28. Pawlak, Z. “Rough Sets: Theoretical Aspects of Reasoning about Data”, Kluwer Academic Publishers, AA Dordrecht, The Netherlands, 1991.

    Google Scholar 

  29. Quinlan, J. R., “Learning Efficient Classification Procedures,” Machine Learning: An Artificial Intelligence Approach, Michalski, R.S., Carbonell, J.G, and Mitchell, T.M. (Eds.), Morgan Kaufmann 1983, pp. 463–482.

    Google Scholar 

  30. Quinlan, J.R., “C4.5: Programs for Machine Learning”, Morgan Kaufmann, San Mateo, CA, 1993.

    Google Scholar 

  31. Reinke, R.E., “Knowledge Acquisition and Refinement Tools for the ADVISE Meta-expert System,” Master's Thesis, University of Illinois, 1984.

    Google Scholar 

  32. Rendell, L., and Seshu, R., “Learning Hard Concepts Through Constructive Induction: Framework and Rationale,” Computer Intelligence, Vol. 6, pp. 247–270, 1990.

    Google Scholar 

  33. Schlimmer, J., “Concept Acquisition Through Representational Adjustment,” Machine Learning, Vol. 1, pp. 81–106, 1986.

    Google Scholar 

  34. Thrun, S.B., Bala, J., Bloedorn, E., Bratko, I., Cestnik, B., Cheng, J., De Jong, K., Dzerowski, S., Fahlman, S.E., Hamann, R., Kaufman, K., Keller, S., Kononenko, I., Kreuziger, J., Michalski, R.S., Mitchell, T., Pachowicz, P., Vafaie, H., Van de Velde, W., Wenzel, W., Wnek, J., and Zhang, J., “The MONK'S Problems: A Performance Comparison of Different Learning Algorithms,” (revised version), Carnegie Mellon University, Pittsburgh, PA, CMU-CS-91-197, 1991.

    Google Scholar 

  35. Utgoff, P., “Shift of Bias for Inductive Learning,”, in Machine Learning: An Artificial Intelligence Approach, Vol. II, R. Michalski, J. Carbonell, and T. Mitchell (eds.), Morgan Kaufman, Los Altos, CA, pp. 107–148, 1986.

    Google Scholar 

  36. Watanabe, L., and Elio, R., “Guiding Constructive Induction for Incremental Learning from Examples,” Proceedings of IJCAI-87, pp. 293–296, Milan, Italy:, 1987.

    Google Scholar 

  37. Weiss, S. M., and Kulikowski, C. A., Computer Systems that Learn, Morgan Kaufmann, San Mateo, CA. 1991.

    Google Scholar 

  38. Wnek, J. and Michalski, R., “Hypothesis-driven Constructive Induction in AQ17-HCI: A Method and Experiments,” Machine Learning, Vol. 14, No. 2, pp. 139–168. 1993.

    Google Scholar 

  39. Wnek, J. “DIAV 2.0 User Manual: Specification and Guide through the Diagrammatic Visualization System,” Reports of the Machine Learning and Inference Laboratory, MLI95-5, George Mason University, Fairfax, VA 1995.

    Google Scholar 

  40. Wnek, I., Kaufman, K., Bloedorn, E., and Michalski, R.S., “Selective Induction Learning System AQ15c: The Method and User's Guide”, Reports of the Machine Learning and Inference Laboratory, MLI 95-4.

    Google Scholar 

  41. Ziarko, W. “On Reduction of Knowledge Representation”, Proceedings of the 2nd International Symposium on Methodologies for Intelligent Systems, Charlotte, NC. North Holland, pp. 99–113.

    Google Scholar 

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Author information

Author notes

  1. Ryszard S. Michalski

    Present address: GMU Departments of Computer Science and Systems Engineering, and the Institute of Computer Science at the Polish Academy of Sciences, Warsaw, Poland

Authors and Affiliations

  1. Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA

    Eric Bloedorn & Ryszard S. Michalski

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  1. Eric Bloedorn
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  2. Ryszard S. Michalski
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Zbigniew W. Raś Maciek Michalewicz

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© 1996 Springer-Verlag Berlin Heidelberg

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Bloedorn, E., Michalski, R.S. (1996). The AQ17-DCI system for data-driven constructive induction and its application to the analysis of world economics. In: Raś, Z.W., Michalewicz, M. (eds) Foundations of Intelligent Systems. ISMIS 1996. Lecture Notes in Computer Science, vol 1079. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61286-6_136

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  • DOI: https://doi.org/10.1007/3-540-61286-6_136

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61286-5

  • Online ISBN: 978-3-540-68440-4

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