一种基于免疫的监督式分类算法

彭凌西; 刘晓洁; 李涛

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一种基于免疫的监督式分类算法

信息工程 | 更新时间：2023-11-28

- 一种基于免疫的监督式分类算法
- An Immune Based Supervised Classifier
- 工程科学与技术 2008年40卷第2期页码：101-106
- 作者机构：
  
  四川大学计算机学院四川,成都,610065
- 作者简介：
- 基金信息：
  
  863计划资助项目(2006AA01Z435);国家自然科学基金资助项目(60573130;60502011);教育部博士点基金资助项目（20070610032）
- DOI：
  中图分类号：
- 纸质出版日期：2008，
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彭凌西,刘晓洁,李涛.一种基于免疫的监督式分类算法[J].工程科学与技术,2008,40(2):101-106.

An Immune Based Supervised Classifier[J]. Advanced Engineering Sciences, 2008,40(2):101-106.
彭凌西,刘晓洁,李涛.一种基于免疫的监督式分类算法[J].工程科学与技术,2008,40(2):101-106. DOI：

An Immune Based Supervised Classifier[J]. Advanced Engineering Sciences, 2008,40(2):101-106. DOI：

摘要

中文摘要: 人工免疫识别系统(AIRS)已被证实为一种高效的分类器

并成功应用于模式识别等领域。然而AIRS存在的记忆细胞数目庞大、分类准确率低等缺陷

限制了进一步的应用。为克服这些缺陷

提出了一种基于免疫的监督式分类算法(AIUC)。AIUC首先初始化记忆细胞;然后通过对每一个训练抗原的学习

进行B细胞进化

在B细胞收敛后

优选出最佳的B细胞对记忆细胞进行更新;最后通过记忆细胞对测试数据进行kNN分类。就数据集Iris、Ionosphere、Diabetes和Sonar分别进行的对比实验结果表明

AIUC比AIRS记忆细胞分别减小了5.6%、18%、19.6%和31%

分类准确率提高到98.2%、96.9%、78.3%和92.3%。该算法具有非线性

以及克隆选择、免疫网络和免疫记忆等生物免疫系统特征

可更好地应用于模式识别、异常检测等领域。

Abstract

Abstract:Artificial immune recognition system (AIRS) had been proved a highly effective classifier

and successfully applied to pattern recognition. However

the huge size of evolved memory cells pool and low classification accuracy limited the further applications of AIRS. In order to overcome these limitations

a supervised artificial immune classifier

referred to as AIUC

was presented. The implementation of AIUC included: initially

a pool of memory cells were created. Then

through the learning of each training antigen

B cell population was evolved until the B cell population was convergent

and the memory cells pool was updated by the optimal B cell. Finally

classification was accomplished by majority vote of the k nearest memory cells. Compared with AIRS

AIUC showed the improvements for the percentages reduction of memory cells pool by 5.6%

18%

19.6% and 31%

respectively

meanwhile

the classification accuracies increased to 98.2%

96.9%

78.3%

and 92.3%

for the famous Iris dataset

the Ionosphere dataset

the Diabetes dataset

and the Sonar dataset

which were used for testing classification algorithm

respectively. In addition to its nonlinear classification properties

AIUC possessed biological immune system properties such as clonal selection

immune network

and immune memory

which could be better used to pattern recognition

anomaly detection.

关键词

人工免疫监督式分类机器学习

Keywords

artificial immune systemsupervised classificationmachine learning

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