  In this paper, we examine the performance of four fuzzy rule generation
methods on Wisconsin breast cancer data. The first method generates fuzzy if
then rules using the mean and the standard deviation of attribute values. The
second approach generates fuzzy if then rules using the histogram of attributes
values. The third procedure generates fuzzy if then rules with certainty of
each attribute into homogeneous fuzzy sets. In the fourth approach, only
overlapping areas are partitioned. The first two approaches generate a single
fuzzy if then rule for each class by specifying the membership function of each
antecedent fuzzy set using the information about attribute values of training
patterns. The other two approaches are based on fuzzy grids with homogeneous
fuzzy partitions of each attribute. The performance of each approach is
evaluated on breast cancer data sets. Simulation results show that the Modified
grid approach has a high classification rate of 99.73 %.
