自然科学版 英文版
自然科学版 英文版
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中南大学学报(自然科学版)

Journal of Central South University

第41卷    第4期    总第194期    2010年8月

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文章编号:1672-7207(2010)04-1485-06
一种用于乳腺癌诊断的免疫分类算法
邓泽林1, 2,谭冠政1,叶吉祥1, 2,范必双1, 2

(1. 中南大学 信息科学与工程学院,湖南 长沙,410083;
2. 长沙理工大学 计算机与通信工程学院,湖南 长沙,410076
)

摘 要: 基于人工免疫识别系统AIRS(Artificial immune recognition system)和核函数提出免疫分类算法Kernel-AIRS。Kernel-AIRS遵循AIRS算法框架,利用核函数将输入空间投影到高维核空间,以核空间距离来度量抗体-抗原的亲和度,提高算法对非线性可分问题的分类准确率。采用Kernel-AIRS定义核空间距离测量方法和规一化方法,分析抗体刺激度和核函数参数与分类准确率之间的关系,研究属性缺失样本对算法分类准确率的影响,并应用Kernel-AIRS算法诊断乳腺癌,分类准确率采用10次交叉验证评价。研究结果表明:Kernel-AIRS算法对排除属性缺失样本数据集分类的准确率为97.3%,对包含属性缺失样本数据集分类的准确率为96.9%,分类准确率较高,适用于乳腺癌的诊断。

 

关键字: 人工免疫识别系统;核函数;分类算法;医疗诊断;乳腺癌

An immune classification algorithm for breast cancer diagnosis
DENG Ze-lin1, 2, TAN Guan-zheng1, YE Ji-xiang1, 2, FAN Bi-shuang1, 2

1. School of Information Science and Engineering, Central South University, Changsha 410083, China;
2. School of Computer and Communication Engineering, Changsha University of Science and Technology,
 Changsha 410076, China

Abstract:Based on an artificial immune recognition system (AIRS) and a kernel function, Kernel-AIRS algorithm was proposed, which followed the AIRS algorithm framework. Using Kernel function to project input space into high dimensional kernel space, the affinity of antibody and antigen was calculated in the kernel space to improve the classification accuracy for the non-linear problems. Kernel space distance computation method and normalization method were defined by Kernel-AIRS, the relationship between the parameters of simulation level and kernel function and classification accuracy was analyzed, and the effect on the classification accuracy of missing attribute samples was discussed. Kernel-AIRS was applied to diagnosis breast caner samples in Wisconsin breast cancer dataset (WBCD), and the classification accuracy was assessed by 10 fold cross validation. The results show that, using this algorithm, the accuracy rates are 97.3% and 96.9% for excluding missing attributes samples and including missing attributes samples respectively, and the classification accuracy is high and applicable for breast cancer diagnosis.

 

Key words: artificial immune recognition system; kernel function; classification algorithm; medical diagnosis; breast cancer

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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