PROCEEDINGS IPMU '08
Prototype-based classification by fuzzification of cases
Parisa KordJamshidi, Bernard De Baets, Guy De Tre.
A fuzzy prototype-based method is
introduced for learning from examples.
A special kind of prototype
vector with fuzzy attributes is derived
for each class from aggregating
fuzzified cases for the purpose
of concept description. The fuzzified
cases are derived by defining a
fuzzy membership function for each
attribute of the sample cases. In a
first method, for the classification of
a new case, the membership degrees
of its crisp attributes to fuzzy aggregated
prototypes are measured.
In a second method, after fuzzifying
the new case, fuzzy set comparison
methods are applied for measuring
the similarity. The methods
are compared to case-based ones like
POSSIBL and kNN using UCI machine
learning repository. We also
make comparisons by using various
transformation methods from probabilities
to possibilities instead of
defining membership functions.
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