PROCEEDINGS IPMU '08
Genetic Feature Selection for Fuzzy Discretized Data
Luciano Sánchez, José R. Villar, Inés Couso
A wrapper-type evolutionary feature
selection algorithm, able to use fuzzy
data, is proposed. In the context
of Genetic Learning of Fuzzy
Rule-based Classifier Systems (FRBCS),
this new algorithm has been
applied to a particular kind of instances,
comprising fuzzy discretized
data (FDD). This data is obtained
when passing crisp data through the
fuzzification interface of the FRBCS
under study.
We have compared the properties
of the algorithm proposed here to
other approaches, over FDD and
crisp data. In case the preprocessed
data is intended to be used by a Genetic
Learning FRBCS, we can conclude
that those algorithms able to
use FDD are preferred over the crisp
ones, even though there is not fuzziness
in the training data being used.
Besides, they also are the only alternative
when the datasets are imprecise,
although this last case is not
elaborated in this study.
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