A Comparative Study of Fuzzy Classification Methods on Breast Cancer Data

TL;DR

This study compares four fuzzy rule generation methods on breast cancer data, highlighting the effectiveness of the modified grid approach which achieves a 99.73% classification accuracy.

Contribution

It introduces and evaluates four fuzzy rule generation techniques, demonstrating the superior performance of the modified grid approach on breast cancer data.

Findings

Modified grid approach achieves 99.73% accuracy

Fuzzy rule methods vary in classification performance

Histogram-based method performs well but less than modified grid

Abstract

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 %.