基于人工蜂群和SVM的基因表达数据分类

doi:10.6040/j.issn.1672-3961.0.2017.405

摘要/Abstract

摘要： 基因表达数据存在高维、小样本、高噪声等特性,使得相应的肿瘤分类诊断面临着一定的挑战。为了实现更加精确的分类准确率,利用人工蜂群(artificial bee colony, ABC)算法对支持向量机(support vector machine, SVM)的核函数参数和惩罚因子进行优化,采用准确率作为分类模型的适应度函数,提出一种基于ABC和SVM的基因表达数据分类方法ABC-SVM。在6种公开的肿瘤基因表达数据集上进行试验,并对比分析其他的分类方法。结果表明,在筛选得到的较少信息基因基础上,ABC-SVM可获得更高的肿瘤分类准确率,对肿瘤样本类型进行更有效的分类预测。

关键词: 人工蜂群, 支持向量机, 智能优化, 肿瘤分类, 生物信息学, 基因表达数据

Abstract: The characteristics of high dimension, small sample and high noise for gene expression data raised many challenges in tumor diagnosis. In order to classify tumor gene expression data more accurately, the kernel function parameters and penalty factors of SVM(support vector machine)were optimized by ABC(artificial bee colony)algorithm, in which classification accuracy was used as the fitness function. A new gene expression data classification method based on ABC algorithm and SVM, which named ABC-SVM, was proposed. Experiments were conducted on six public tumor gene expression datasets, and other classicfication methods were compared. The results showed that ABC-SVM, a method based on fewer informative genes, could obtain higher classification accuracy, and the classification of tumor samples could be more effectively predicted.

Key words: artificial bee colony, support vector machine, gene expression data, tumor classification, bioinformatics, intelligent optimization

中图分类号:

TP391

叶明全,高凌云,万春圆. 基于人工蜂群和SVM的基因表达数据分类[J]. 山东大学学报(工学版), 2018, 48(3): 10-16.

YE Mingquan, GAO Lingyun, WAN Chunyuan. Gene expression data classification based on artificial bee colony and SVM[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 10-16.

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