In this paper, we examined the performance of four fuzzy
rule generation methods that could generate fuzzy if-then
rules directly from training patterns with no timeconsuming
tuning procedures. In the first approach, a
single fuzzy if-then rule was generated for each class using
the mean and the standard deviation of attribute values. In
the second approach, a single fuzzy if-then rule was
generated for each class using the histogram of attribute
values. The third approach generated fuzzy if-then rules by
homogeneously partitioning each attribute. Thus, a pattern
space was partitioned into a simple fuzzy grid. The
information about attribute values was not used for
specifying the membership function of each antecedent
fuzzy set. The local information of training patterns was
utilized when the consequent class and the certainty grade
were specified. The last approach was a modified version
of the simple fuzzy grid approach.