Rule induction

Rule induction is an area of machine learning in which formal rules are extracted from a set of observations. The rules extracted may represent a full scientific model of the data, or merely represent local patterns in the data.

Paradigms

Some major rule induction paradigms are:

  • Association rule learning algorithms (e.g., Agrawal)
  • Decision rule algorithms (e.g., Quinlan 1987)
  • Hypothesis testing algorithms (e.g., RULEX)
  • Horn clause induction
  • Version spaces
  • Rough set rules
  • Inductive Logic Programming
  • Boolean decomposition (Feldman)

Algorithms

Some rule induction algorithms are:

gollark: Also you need four presses. There are four.
gollark: <@151391317740486657> in a sense.
gollark: Tech mods often add generator things.
gollark: They're important utility items for... making holes in things, and moving them.
gollark: It would be foolish to not have a pickaxe.

References
  1. Sahami, Mehran. "Learning classification rules using lattices." Machine learning: ECML-95 (1995): 343-346.
  • Quinlan, J. R. (1987). "Generating production rules from decision trees" (PDF). In McDermott, John (ed.). Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-87). Milan, Italy. pp. 304–307.
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