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  • Specialization algorithms start from very general descriptions and specializes those until they are correct.
  • This is done by adding additional premises to the antecedent of a rule, or by restricting the range of an attribute which is used in an antecedent.

  • Algorithms relying on specialization generally have the problem of overspecialization: previous specialization steps could become unnecessary due to subsequent specialization steps.
  • This brings along the risk for ending up with results that are not maximal-general.

  • Some examples of (heuristic) specialization algorithms are the following: ID3, AQ, C4, CN2, CABRO, FOIL, or PRISM; references at the end of the lecture.

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