一种基于动态类中心模型选择的模糊支持向量机

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宋一明,鞠哲.一种基于动态类中心模型选择的模糊支持向量机[J].,2023,63(2):199-204

一种基于动态类中心模型选择的模糊支持向量机

Fuzzy support vector machine based on dynamic class-center model selection

DOI：10.7511/dllgxb202302011

中文关键词:模糊支持向量机隶属度函数分类粒子群算法萤火虫算法

英文关键词:fuzzy support vector machine membership function classification particle swarm optimization algorithm firefly algorithm

基金项目:辽宁省自然科学基金资助项目(2019-BS-187)；辽宁省教育厅项目(JYT19027).

作者	单位
宋一明,鞠哲

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中文摘要:

模糊支持向量机的核心思想是赋予样本模糊隶属度，给每个样本以不同的权重，从而克服标准支持向量机对噪声和异常点敏感的问题．现有的模糊支持向量机算法通常以样本与类中心距离为基础，给每个样本赋予一个固定的隶属度，没有根据样本分布对隶属度做进一步修正．提出了一种新的动态方式赋予样本隶属度，利用萤火虫算法不断地更新样本中心的位置和隶属度函数，同时利用粒子群算法优化模糊支持向量机参数．在UCI数据集上的实验结果表明，该算法可以有效减少噪声和野点对超平面的影响，分类性能要优于几类常用的模糊支持向量机算法．

英文摘要:

The core idea of fuzzy support vector machine is to give the fuzzy membership to the samples and give different weights to each sample, so as to overcome the problems that the standard support vector machine is sensitive to noise and outliers. The existing fuzzy support vector machine algorithms usually assign a fixed membership to each sample based on the distance between the sample and the class-center, without further modifying the membership according to the sample distribution. A new dynamic mode is proposed to assign membership to the sample. The firefly algorithm is used to update the position and membership function of the sample center constantly, and the particle swarm optimization algorithm is used to optimize the parameters of fuzzy support vector machine. Experimental results on UCI dataset show that the proposed algorithm can effectively reduce the influence of noise and wild points on the hyperplane, and the classification performance is better than that of several common fuzzy support vector machine algorithms.

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一种基于动态类中心模型选择的模糊支持向量机

本站小编 Free考研考试/2024-01-16

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