Inclusive pruning: A new class of pruning rule for unordered search and its
application to classification learning.
School of Computing and Mathematics,
Geelong, 3217, Australia.
This paper presents a new class of pruning rule for unordered search. Previous
pruning rules for unordered search identify operators that should not be
order to prune nodes reached via those operators. In contrast, the new
identify operators that should be applied and prune nodes that are not reached
those operators. Specific pruning rules employing both these approaches are
identified for classification learning. Experimental results demonstrate that
application of the new pruning rules can reduce by more than 60\% the number
from the search space that are considered during classification learning.
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