Two-stage classification methods for microarray data

Tzu-Tsung Wong, Ching Han Hsu

研究成果: Article同行評審

37 引文 斯高帕斯(Scopus)

摘要

Gene expression data are a key factor for the success of medical diagnosis, and two-stage classification methods are therefore developed for processing microarray data. The first stage for this kind of classification methods is to select a pre-specified number of genes, which are likely to be the most relevant to the occurrence of a disease, and passes these genes to the second stage for classification. In this paper, we use four gene selection mechanisms and two classification tools to compose eight two-stage classification methods, and test these eight methods on eight microarray data sets for analyzing their performance. The first interesting finding is that the genes chosen by different categories of gene selection mechanisms are less than half in common but result in insignificantly different classification accuracies. A subset-gene-ranking mechanism can be beneficial in classification accuracy, but its computational effort is much heavier. Whether the classification tool employed at the second stage should be accompanied with a dimension reduction technique depends on the characteristics of a data set.

原文English
頁(從 - 到)375-383
頁數9
期刊Expert Systems With Applications
34
發行號1
DOIs
出版狀態Published - 2008 一月 1

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications

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