A comparative study of fuzzy classification methods on breast cancer data.
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 with 92.2% correct classification rate. The second approach generates fuzzy if-then rules using the histogram of attributes values with 86.7% correct classification rate. The third procedure generates fuzzy if-then rules with certainty of each attribute into homogeneous fuzzy sets with 99.73% correct classification rate. In the fourth approach, only overlapping areas are partitioned with 62.57% correct classification rate. 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 simple grid approach has a high classification rate of 99.73%.
Jain
R
R
School of Computer & Information Science, University of South Australia, Mawson Lakes, Australia. rkjain@bigpond.net.au
Abraham
A
A
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000145:classification* / Q000175:diagnosis*; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity