基于模拟退火的模糊分类系统在数据挖掘中的应用  

Application Research of Fuzzy Classification System Based on Simulated Annealing

作　　者：郑晓月[1] 康鲲鹏[1] 

机构地区：[1]商丘师范学院计算机科学系,河南商丘476000

出　　处：《河南师范大学学报（自然科学版）》2010年第1期51-55,共5页Journal of Henan Normal University(Natural Science Edition)

基　　金：河南省教育厅自然科学研究计划项目(2009A520020)

摘　　要：给出了一种基于模拟退火的模糊分类系统—SAFCS,该分类系统结合了SA元启发式搜索策略的学习能力和模糊系统的近似推理方法,旨在改善与分类问题有关的大型数据空间的搜索性能,找到模糊if-then规则的优化集.SAFCS可以从输入数据集中抽取精确的模糊分类规则,并在若干不同预定义类中将其应用于对新数据实例的分类.文末用某数据集检测了SAFCS的性能,结果表明,在与其他几个著名算法比较时该分类系统性能可靠.A Simulated annealing based fuzzy classification system （SAFCS） is presented in the paper,which hybridizes the learning capability of SA metaheuristic with the approximate reasoning method of fuzzy systems. The objective of SAFCS is to effectively explore the large search space usually associated with classification problems,and find the optimum set of fuzzy if-then rules. The SAFCS would be able to extract accurate fuzzy classification rules from input data sets,and applies them to classify new data instances in different predefined groups or classes. Experiments are performed with some data sets. The results indicate that the proposed SAFCS achieves competitive results in comparison with several well-known classification algorithms.

关 键 词：模拟退火 数据挖掘 模糊系统 模糊规则抽取 

分 类 号：TP273.4[自动化与计算机技术—检测技术与自动化装置]