TY  - JOUR
T1  - A Hybrid Particle Swarm Optimization and Fuzzy Rule-Based System for Breast Cancer Diagnosis
AU - Alikar, Najmeh AU - Abdullah, Salwani AU - Mousavi, Seyed Mohsen AU - Niaki, Seyed Taghi Akhavan 
JO  - International Journal of Soft Computing
VL  - 8
IS  - 2
SP  - 126
EP  - 133
PY  - 2013
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2013.126.133
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2013.126.133
KW  - Particle swarm optimization
KW  -fuzzy rule based
KW  -breast cancer diagnosis
KW  -Wisconsin breast cancer dataset
KW  -Taguchi Method
AB  - A hybrid algorithm of a particle swarm optimization and a fuzzy rule-based classification system is proposed in this study to diagnose breast cancer. Two orthogonal and triangular types of fuzzy sets are applied to represent the input variables. In additional, different input membership functions are considered to increase the classification accuracy. The performance of the proposed hybrid algorithm is studied using a classification accuracy measure on the Wisconsin breast cancer dataset. The results of the comparison using different training data sets show the higher performance of the proposed methodology.
ER  -