用于不平衡数据分类的0阶TSK型模糊系统

摘要

处理不平衡数据分类时,传统模糊系统对少数类样本识别率较低.针对这一问题,首先,在前件参数学习上,提出了竞争贝叶斯模糊聚类(Bayesian fuzzy clustering based on competitive learning,BFCCL)算法,BFCCL算法考虑不同类别样本聚类中心间的排斥作用,采用交替迭代的执行方式并通过马尔科夫蒙特卡洛方法获得模型参数最优解.其次,在后件参数学习上,基于大间隔的策略并通过参数调节使得少数类到分类面的距离大于多数类到分类面的距离,该方法能有效纠正分类面的偏移.基于上述思想以0阶TSK型模糊系统为具体研究对象构造了适用于不平衡数据分类问题的0阶TSK型模糊系统(0-TSK-IDC).人工和真实医学数据集实验结果表明,0-TSK-IDC在不平衡数据分类问题中对少数类和多数类均具有较高的识别率,且具有良好的鲁棒性和可解释性.%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.