期刊
COMPUTATIONAL STATISTICS & DATA ANALYSIS
卷 48, 期 4, 页码 869-885出版社
ELSEVIER
DOI: 10.1016/j.csda.2004.03.017
关键词
microarray; classification; feature selection
Since most classification articles have applied a single technique to a single gene expression dataset, it is crucial to assess the performance of each method through a comprehensive comparative study. We evaluate by extensive comparison study extending Dudoit et at. (J. Amer. Statist. Assoc. 97 (2002) 77) the performance of recently developed classification methods in microarray experiment, and provide the guidelines for finding the most appropriate classification tools in various situations. We extend their comparison in three directions: more classification methods (21 methods), more datasets (7 datasets) and more gene selection techniques (3 methods). Our comparison study shows several interesting facts and provides the biolopsts and the biostatisticians some insights into the classification tools in microarray data analysis. T-his study also shows that the more sophisticated classifiers give better performances than classical methods such as kNN, DLDA DQDA and the choice of gene selection method has much effect on the performance of the classification methods, and thus the classification methods should be considered together with the gene selection criteria. (C) 2004 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据