自动化学报2017，Vol.43Issue(10)：1773-1788,16.DOI:10.16383/j.aas.2017.c160200
用于不平衡数据分类的0阶TSK型模糊系统
Zero-order TSK-type Fuzzy System for Imbalanced Data Classification
摘要
Abstract
When learning from imbalanced datasets,the traditional fuzzy systems have a low rate of identification over the minority class.Firstly,in the antecedent parameter learning stage,a new clustering method,called Bayesian fuzzy clustering based on competitive learning (BFCCL),is proposed to partition the input space for the antecedents of if-then rules.BFCCL considers the repulsed force of clustering prototypes between different classes,and uses an alternating iterative strategy to obtain the optimal model parameters by Markov chain Monte Carlo method.Secondly,in the consequent parameter learning stage,based on the maximum separation strategy and by keeping the distance between the minority class and the classification hyperplane larger than the distance between the majority class and the hyperplane,the method can effectively correct the skewness of the classification hyperplane.Based on the above ideas,a zero-orderTakagi-Sugeno-Kang fuzzy system for imbalanced data classification (0-TSK-IDC) is proposed.Experimental results on artificial and real-world medicine datasets illustrate the effectiveness of 0-TSK-IDC on both minority and majority classes in imbalanced data classification,as well as its good robustness and interpretability.关键词
不平衡数据/分类/马尔科夫蒙特卡洛/Takagi-Sugeno-Kang型模糊系统Key words
Imbalanced data/classifying/Markov chain Monte Carlo/Takagi-Sugeno-Kang type fuzzy system引用本文复制引用
顾晓清,蒋亦樟,王士同..用于不平衡数据分类的0阶TSK型模糊系统[J].自动化学报,2017,43(10):1773-1788,16.基金项目
国家自然科学基金(61502058,61572085,61572236),江苏省自然科学基金资助(BK20160187),中央高校基本科研业务费专项资金资助项目(JUSRP51614A)资助 （61502058,61572085,61572236）
Supported by National Natural Science Foundation of China(61502058,61572085,61572236),Natural Science Foundation of Jiangsu Province under Grant (BK20160187),and Fundamental Research Funds for the Central Universities (JUSRP51614A) （61502058,61572085,61572236）
