Title :
Optimization of fuzzy classification system by genetic strategies
Author :
Zhang, Xiang ; Zhang, Huaixiang ; Li, Ertao
Author_Institution :
Sch. of Comput., Hangzhou Dianzi Univ., Hangzhou, China
Abstract :
A novel approach to construct fuzzy classification system based on fuzzy association rules is proposed in this paper. Competitive agglomeration algorithm is employed to partition quantitative attributes from each data record into several optimized fuzzy sets, resulting in an initial fuzzy classification system. A fuzzy classification system with high accuracy and interpretability can be further achieved by genetic strategies. Simulation applied to an existent diabetes dataset demonstrates the performance of the proposed approach is better than those of other popular classification methods.
Keywords :
authorisation; fuzzy set theory; genetic algorithms; medical computing; pattern classification; competitive agglomeration algorithm; diabetes dataset; fuzzy association rules; fuzzy classification system; genetic strategy; optimized fuzzy set theory; partition quantitative attributes; Accuracy; Association rules; Classification algorithms; Databases; Fuzzy sets; Partitioning algorithms; classification system; fuzzy logic; genetic algorithm;
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
DOI :
10.1109/ICNC.2010.5583508
