کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10328160 | 681636 | 2005 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An extensive comparison of recent classification tools applied to microarray data
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
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 al. (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 biologists and the biostatisticians some insights into the classification tools in microarray data analysis. This 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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 48, Issue 4, 1 April 2005, Pages 869-885
Journal: Computational Statistics & Data Analysis - Volume 48, Issue 4, 1 April 2005, Pages 869-885
نویسندگان
Jae Won Lee, Jung Bok Lee, Mira Park, Seuck Heun Song,