一种改进的模糊人工免疫网络数据分类方法

苟世宁; 杜海峰; 栗茂林; 庄健

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一种改进的模糊人工免疫网络数据分类方法

专题研究 | 更新时间：2025-12-08

- 一种改进的模糊人工免疫网络数据分类方法
- Data Classification Based on a Modified Fuzzy Artificial Immune Network
- 西安交通大学学报 2007年41卷第5期页码：585-588+620
- 作者机构：
  
  西安交通大学现代设计和转子轴承系统教育部重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2006-09-18. 作者简介: 苟世宁(1983～),男,硕士生;杜海峰(联系人),男,副教授. 基金项目: 国家自然科学基金资助项目(50505034)
- DOI：
  中图分类号： TP183
- 网络首发：2007-05-10，
  
  纸质出版：2007
- 稿件说明：
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苟世宁, 杜海峰, 栗茂林, 等. 一种改进的模糊人工免疫网络数据分类方法[J]. 西安交通大学学报, 2007,41(5):585-588+620.

苟世宁, 杜海峰, 栗茂林, et al. Data Classification Based on a Modified Fuzzy Artificial Immune Network[J]. 2007, 41(5): 585-588+620.
苟世宁, 杜海峰, 栗茂林, 等. 一种改进的模糊人工免疫网络数据分类方法[J]. 西安交通大学学报, 2007,41(5):585-588+620. DOI：

苟世宁, 杜海峰, 栗茂林, et al. Data Classification Based on a Modified Fuzzy Artificial Immune Network[J]. 2007, 41(5): 585-588+620. DOI：

摘要

针对现有人工免疫网络算法对先验知识应用不足的问题

提出一种基于模糊人工免疫网络的有监督学习数据分类方法.首先采用模糊C均值聚类算法为免疫网络提供疫苗(初始种群)

将此疫苗作为免疫网络的初始抗体群

种群再经过克隆选择、网络压缩、免疫成熟、记忆等算子的不断扩展和压缩

形成一个由浓缩后的训练数据构成的抗体网络

最终基于该抗体网络采用“邻近原则”构造分类器.由于各算子的协调作用

该方法能够在高浓缩率的情况下更好地代替样本空间.UCI(University of California

Irvine)数据集的仿真实验证明

与aiNet方法相比

该方法在分类准确率和数据浓缩率上分别高出7.26%和11.16%

而且更稳定、可靠.

Abstract

Based on fuzzy theory and artificial immune network

a new data classification method

named fuzzy artificial immune network classification(FAINC)

is put forward. In order to improve the convergence rate

fuzzy C mean clustering algorithm is employed to provide vaccines(initial population)for artificial immune network. The operators such as clonal selection

network compression

immune maturation and immune memory are explored. According to expanding and compressing the population of the network

a stable antibody network can be acquired. Namely

the antibody network represents the concentrated training data to construct the classificator. The simulation experiments of University of California

Irvine(UCI)data sets validate the higher classification accuracy

data enrichment

stability and reliability of FAINC than aiNet.

关键词

Keywords

references

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