Probabilistic rule generator
Won D. Lee, Sylvian R. Ray · 1986
Probabilistic Rule Generator(PRG) generates rules by inductive inference from training examples.It employs information-theoretic entropy as a criterion in building a search tree, but also uses multiple-valued logic to expand a captured complex.This study shows that PRG efficiently captures major clusters and is more general than former inductive inference algorithms in providing probabilistic features.A brief discussion of how PRG can systematically modify initial hypotheses is also presented.