基于极速学习的Choquet模糊积分分类器  

Choquet fuzzy integral classifier based on extreme learning machine

作　　者：陈爱霞[1] 张春琴[1] CHEN Aixia;ZHANG Chunqin(Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province, College of Mathematics and Information Science, Hebei University, Baoding 071002, China)

机构地区：[1]河北大学数学与信息科学学院河北省机器学习与计算智能重点实验室

出　　处：《河北大学学报（自然科学版）》2019年第4期337-341,共5页Journal of Hebei University(Natural Science Edition)

基　　金：国家自然科学基金资助项目(61672205);河北省教育厅青年基金资助项目(QN2018161)

摘　　要：在交互环境下,模糊积分分类器具有良好的分类性能.如何确定在属性集幂集上定义的模糊测度是模糊积分分类器中的一个关键问题.当属性的个数增加时,计算复杂度呈指数级增长.为了解决这一问题,借鉴极速学习机算法中权重向量随机确定的思想,提出了ELM-Choquet模糊积分分类器.实验结果表明,和Choquet模糊积分分类器相比,该算法具有较优的分类性能.In the interactive environments,fuzzy integral classifier has shown its good classification performance. A key problem in the fuzzy integral classifier is to determine the fuzzy measure defined in the power set of attributes. The computational complexity increases exponentially as the number of attributes increases. In order to solve the problem, drawing lessons from the idea of random determination of weight vector in extrem learning machine algorithm, this paper puts forward an ELM-Choquet fuzzy integral classifier. The experiment result shows that compared with the Choquet fuzzy integral classifier, this algorithm has better classification performance.

关 键 词：模糊测度 模糊积分 CHOQUET模糊积分 极速学习机 遗传算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]