Zusammenfassung
Constructive Induction is the process of transforming
the original representation of hard concepts with
complex interaction into a representation that
highlights regularities. Most Constructive Induction
methods apply a greedy strategy to find interacting
attributes and then construct functions over them. This
approach fails when complex interaction exists among
attributes and the search space has high variation. In
this paper, we illustrate the importance of applying
Genetic Algorithms as a global search strategy for
these methods and present MFE2/GA1, while comparing it
with other GA-based Constructive Induction methods. We
empirically analyse our Genetic Algorithm's operators
and compare MFE2/GA with greedy-based methods. We also
performed experiments to evaluate the presented method
when concept has attributes participating in more than
one complex interaction. In experiments that are
conducted, MFE2/GA successfully finds interacting
attributes and constructs functions to represent
interactions. Results show the advantage of using
Genetic Algorithms for Constructive Induction when
compared with greedy-based methods.
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