JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2018, Vol. 48 ›› Issue (3): 10-16.doi: 10.6040/j.issn.1672-3961.0.2017.405

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Gene expression data classification based on artificial bee colony and SVM

YE Mingquan, GAO Lingyun, WAN Chunyuan   

  1. Research Center of Health Big Data Mining and Applications, Wannan Medical College, Wuhu 241002, Anhui, China
  • Received:2017-05-09 Online:2018-06-20 Published:2017-05-09

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

CLC Number: 

  • TP391
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