Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Structured Induction

  • Michael Bain
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_796


Structured induction is a method of applying machine learning in which a model for a task is learned using a representation where some of the components are themselves the outputs of learned models for specified sub-tasks. The idea was inspired by structured programming (Dahl, Dijkstra and Hoare, 1972), in which a complex task is solved by repeated decomposition into simpler sub-tasks that can be easily analyzed and implemented. The approach was first developed by Alen Shapiro (1987) in the context of constructing expert systems by  decision tree learning, but in principle it could be applied using other learning methods.

Motivation and Background

Structured induction is designed to solve complex learning tasks for which it is difficult a priori to obtain a set of attributes or features in which it is possible to represent an accurate approximation of the target hypothesis reasonably concisely. In Shapiro’s approach, a hierarchy of  decision treesis learned, where in each...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. Dahl, O. J., Dijkstra, E. W., & Hoare, C. A. R. (Eds.). (1972). Structured programming. London: Academic Press.zbMATHGoogle Scholar
  2. Feigenbaum, E. A. (1977). The art of artificial intelligence: Themes and case studies of knowledge engineering. In R. Reddy (Ed.), Proceedings of the fifth international conference on artificial intelligence (IJCAI77) (pp. 1014–1029). Los Altos, CA: William Kaufmann.Google Scholar
  3. Gaines, B. (1996). Transforming rules and trees into comprehensible knowledge structures. In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Eds.), Advances in knowledge discovery and data mining (pp. 205–226). Cambridge, MA: MIT Press.Google Scholar
  4. Michie, D. (1982). Measuring the knowledge-content of expert programs. Bulletin of the Institute of Mathematics and its Applications, 18(11/12), 216–220.MathSciNetGoogle Scholar
  5. Muggleton, S. (1987). Duce, an oracle-based approach to constructive induction. In IJCAI 87 (pp. 287–292). Los Altos, CA: Kaufmann.Google Scholar
  6. Pagallo, G., & Haussler, D. (1990). Boolean feature discovery in empirical learning. Machine learning, 5, 71–99.CrossRefGoogle Scholar
  7. Quinlan, J. R. (1983). Learning efficient classification procedures and their application to chess end games. In R. Michalski, J. Carbonnel, & T. Mitchell (Eds.), Machine learning: An artificial intelligence approach, (pp. 464–482). Palo Alto, CA: Tioga.Google Scholar
  8. Razzak, M. A., Hassan, T., & Pettipher, R. (1984). Extran-7: A Fortran-based software package for building expert systems. In M. A. Bramer (Ed.), Research and development in expert systems (pp. 23–30). Cambridge: Cambridge University Press.Google Scholar
  9. Shapiro, A., & Niblett, T. (1982). Automatic induction of classification rules for a chess endgame. In M. R. B. Clarke (Ed.), Advances in computer chess (Vol. 3, pp. 73–91). Pergamon: Oxford.Google Scholar
  10. Shapiro, A. D. (1987). Structured Induction in expert systems. Wokingham: Turing Institute Press with Addison Wesley.zbMATHGoogle Scholar
  11. Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge: Cambridge University Press.Google Scholar
  12. Zhang, J., & Honavar, V. (2003). Learning decision tree classifiers from attribute value taxonomies and partially specified data. In ICML-2003: Proceedings of the twentieth international conference on machine learning, Menlo Park, CA: AAAI Press.Google Scholar
  13. Zheng, Z. (1995). Constructing nominal X-of-N attributes. In Proceedings of the fourteenth International joint conference on artificial intelligence (IJCAI, 95) (pp. 1064–1070). Los Altos, CA: Morgan Kaufmann.Google Scholar
  14. Zupan, B., Bohanec, M., Demsar, J., & Bratko, I. (1999). Learning by discovering concept hierarchies. Artificial Intelligence, 109, 211–242.MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Michael Bain

There are no affiliations available