基于直觉模糊集的集成学习算法  

Ensemble Learning Algorithm Based on Intuitionistic Fuzzy Sets

作　　者：戴宗明 胡凯 谢捷 郭亚 DAI Zong-ming;HU Kai;XIE Jie;GUO Ya(Key Laboratory of Advanced Process Control for Light Industry,Ministry of Education,Jiangnan University,Wuxi,Jiangsu 214122,China;School of Internet of Things,Jiangnan University,Wuxi,Jiangsu 214122,China)

机构地区：[1]江南大学轻工业先进过程控制教育部重点实验室,江苏无锡214122 [2]江南大学物联网工程学院,江苏无锡214122

出　　处：《计算机科学》2021年第S01期270-274,280,共6页Computer Science

基　　金：国家自然科学基金项目(71904064);测绘遥感信息工程国家重点实验室重点开放基金项目(18I04);江苏省自然科学基金(BK20190580);中央高校自主科研基金青年项目(JUSRP11922)。

摘　　要：为提高传统机器学习算法的分类精度和泛化能力,提出一种基于直觉模糊集的集成学习算法。根据传统分类器分类精度构建直觉模糊偏好关系矩阵,确定分类器权重,结合多属性群决策方法确定样本分类结果。在UCI中的7个数据集上进行测试,与目前流行的传统分类算法以及集成学习分类算法SVM,LR,NB,Boosting,Bagging相比,提出的算法分类平均精度分别提升了1.91%,3.89%,7.80%,3.66%,4.72%。该算法提高了传统分类方法的分类精度和泛化能力。In order to improve the classification accuracy and generalization ability of traditional machine learning algorithms,this paper proposes an ensemble learning algorithm based on intuitionistic fuzzy sets(IFS-EL).The algorithm constructs an intuitionistic fuzzy preference relation(IFPR)matrix according to the classification accuracy of the traditional classifier.The matrix is used to determine the weights of the classifiers and the multi-criteria group decision making(MCGDM)is used to determine the sample classification result.The experimental data uses 7 classification data sets in UCI,and the training set and test set are divided into 7:3.The classification results are compared with the current popular traditional classification algorithms and ensemble learning classification algorithms,SVM,LR,NB,Boosting,Bagging,the average accuracy of the algorithm in this paper is improved by 1.91%,3.89%,7.80%,3.66%,4.72%.The experimental results show that the IFS-EL can improve the classification accuracy and generalization ability.

关 键 词：直觉模糊集 集成学习 分类 多属性群决策 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]