A NEW CLUSTERING-BASED APPROACH FOR MODELING FUZZY RULE-BASED CLASSIFICATION SYSTEMS

In the present study, we propose a novel clustering-based method for modeling accurate
fuzzy rule-based classification systems. The new method is a combination of a data mapping
method, subtractive clustering method and an efficient gradient descent algorithm. A data mapping
method considers the intricate geometric relationships that may exist among the data and computes
a new representation of data that optimally preserves local neighbourhood information in a certain
sense. The approach uses subtractive clustering method to extract the fuzzy classification rules
from data; the rule parameters are then optimized by using an efficient gradient descent algorithm.
Twenty datasets taken from UCI repository are employed to compare the performance of the
proposed approach with the other similar existing classifiers. Some non-parametric statistical tests
are utilized to compare the results obtained in experiments. The statistical comparisons confirm the
superiority of the proposed method compared to other similar classifiers, both in terms of
classification accuracy and computational effort.