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 %.