Abstract
A data mining procedure for automatic determination of
fuzzy decision tree structure using a genetic program
is discussed. A genetic program (GP) is an algorithm
that evolves other algorithms or mathematical
expressions. Methods for accelerating convergence of
the data mining procedure are examined. The methods
include introducing fuzzy rules into the GP and a new
innovation based on computer algebra. Experimental
results related to using computer algebra are given.
Comparisons between trees created using a genetic
program and those constructed solely by interviewing
experts are made. Connections to past GP based data
mining procedures for evolving fuzzy decision trees are
established. Finally, experimental methods that have
been used to validate the data mining algorithm are
discussed.
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