Mining and integrating reliable decision rules for imbalanced cancer gene expression data sets

被引：0

作者：

Yu, Hualong ^{[1
]}

Ni, Jun ^{[2
]}

Dan, Yuanyuan ^{[3
]}

Xu, Sen ^{[4
]}

机构：

[1] School of Computer Science and Engineering, Jiangsu University of Science and Technology

[2] Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City

[3] School of Biology and Chemical Engineering, Jiangsu University of Science and Technology

[4] School of Information Engineering, Yancheng Institute of Technology

来源：

Tsinghua Science and Technology | 2012年 / 17卷 / 06期

关键词：

cancer gene expression data; class imbalance; decision rule; ensemble learning; majority voting; paired differential expression genes;

D O I：

10.1109/TST.2012.6374368

中图分类号：

学科分类号：

摘要：

There have been many skewed cancer gene expression datasets in the post-genomic era. Extraction of differential expression genes or construction of decision rules using these skewed datasets by traditional algorithms will seriously underestimate the performance of the minority class, leading to inaccurate diagnosis in clinical trails. This paper presents a skewed gene selection algorithm that introduces a weighted metric into the gene selection procedure. The extracted genes are paired as decision rules to distinguish both classes, with these decision rules then integrated into an ensemble learning framework by majority voting to recognize test examples; thus avoiding tedious data normalization and classifier construction. The mining and integrating of a few reliable decision rules gave higher or at least comparable classification performance than many traditional class imbalance learning algorithms on four benchmark imbalanced cancer gene expression datasets. © 1996-2012 Tsinghua University Press.

引用

页码：666 / 673

页数：7

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