基于迭代模糊聚类算法与K近邻和数据字典的集成TSK模糊分类器  

Iterative Fuzzy C-means Clustering Algorithm & K-Nearest Neighbor and Dictionary Data Based Ensemble TSK Fuzzy Classifiers

作　　者：张雄涛 蒋云良[2] 潘兴广 胡文军[2] 王士同[1] ZHANG Xiongtao;JIANG Yunliang;PAN Xingguang;HU Wenjun;WANG Shitong(School of Digital Media,Jiangnan University,Wuxi 214122,China;School of Information Engineering,Huzhou University,Huzhou 313000,China;Engineer Training Center,Guizhou Minzu University,Guiyang 550025,China)

机构地区：[1]江南大学数字媒体学院,无锡214122 [2]湖州师范学院信息工程学院,湖州313000 [3]贵州民族大学工程实训中心,贵阳550025

出　　处：《电子与信息学报》2020年第3期746-754,共9页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61572236,61300151,61772198,61771193);中央高校基本科研业务费专项资金(JUDCF13030)~~

摘　　要：该文提出一种新型的集成TSK模糊分类器(IK-D-TSK),首先通过并行学习的方式组织所有0阶TSK模糊子分类器,然后每个子分类器的输出被扩充到原始(验证)输入空间,最后通过提出的迭代模糊聚类算法(IFCM)作用在增强验证集上生成数据字典,从而利用KNN对测试数据进行快速预测。IK-D-TSK具有以下优点:在IK-DTSK中,每个0阶TSK子分类器的输出被扩充到原始入空间,以并行方式打开原始(验证)输入空间中存在的流形结构,根据堆栈泛化原理,可以保证提高分类精度;和传统TSK模糊分类器相比,IK-D-TSK以并行方式训练所有的子分类器,因此运行速度可以得到有效保证;由于IK-D-TSK是在以IFCM&KNN所获得的数据字典的基础上进行分类的,因此具有强鲁棒性。理论和实验验证了模糊分类器IK-D-TSK具有较高的分类性能、强鲁棒性和高可解释性。A new ensemble TSK fuzzy classifier(i,e. IK-D-TSK) is proposed. First, all zero-order TSK fuzzy sub-classifiers are organized in a parallel way, then the output of each sub-classifier is augmented to the original(validation) input space, finally, the proposed Iterative Fuzzy C-Means(IFCM) clustering algorithm generates dictionary data on augmented validation dataset, and then KNN is used to predict the result for test data. IKD-TSK has the following advantages: the output of each zero-order TSK subclassifier is augmented to the original input space to open the manifold structure in parallel, according to the principle of stack generalization,the classification accuracy can be improved;Compared with traditional TSK fuzzy classifiers which trains sequentially, IK-D-TSK trains all the sub-classifiers in parallel, so the running speed can be effectively guaranteed;Because IK-D-TSK works based on dictionary data obtained by IFCM & KNN, it has strong robustness. The theoretical and experimental results show that IK-D-TSK has high classification performance,strong robustness and high interpretability.

关 键 词：TSK模糊分类器 迭代模糊聚类算法 数据字典 可解释性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]