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Knowledge Discovery and Induction of Decision Trees in Spatial Decision Problems

In: Spatial Economic Science

Author

Listed:
  • Jean-Claude Thill

    (State University of New York at Buffalo)

  • Aaron Wheeler

    (University of New Mexico)

Abstract

Machine learning, a branch of artificial intelligence, investigates the mechanisms by which knowledge is acquired through experience. A large number of machine learning methods and algorithms have been developed, including neural computing (Freeman and Skapura 1991), case-based reasoning (Kolodner 1993), genetic algorithms (Goldberg 1989), and inductive learning (Quinlan 1988). These approaches form the essential toolbox of methods to extract useful information from data sets built into the knowledge base of expert systems. It has been argued that these computational methods are not only useful for the design and implementation of effective and efficient decision support and expert systems, but also as support tools in furthering scientific knowledge discovery above and beyond what conventional methods of inquiry have so far permitted. In the domain of the Spatial Sciences, this viewpoint is forcefully advocated in the research white paper on ‘Spatial Analysis in a GIS Environment’ of the University Consortium for Geograhic Information Science (UCGIS 1997).

Suggested Citation

  • Jean-Claude Thill & Aaron Wheeler, 2000. "Knowledge Discovery and Induction of Decision Trees in Spatial Decision Problems," Advances in Spatial Science, in: Aura Reggiani (ed.), Spatial Economic Science, chapter 10, pages 188-205, Springer.
    • Handle: RePEc:spr:adspcp:978-3-642-59787-9_10
    DOI: 10.1007/978-3-642-59787-9_10
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    Cited by:

    1. Suel, Esra & Polak, John W., 2017. "Development of joint models for channel, store, and travel mode choice: Grocery shopping in London," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 147-162.

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