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Mining and Integrating Reliable Decision Rules for Imbalanced Cancer Gene Expression Data Sets

Tsinghua Science and Technology 2012年第6期17卷 666-673页

作者： Hualong Yu 1 , jun ni 2., yuanyuan dan 3., {2. xu {{4. school of computer Science and engineering, jiangsu university of Science and Technology, zhenjiang 2.2.03. china 2. Department of Radiology, Carver College of Medicine, The university of Iowa, Iowa City, IA 52.4.2} USA {4. school of Biology and Chemical engineering, jiangsu university of Science and Technology, zhenjiang 2.2.03. china 4. school of Information engineering, Yancheng Institute of Technology, Yancheng 2.4.51, {24. school of computer Science and {2. Jiangsu University of Science and Technology Zhenjiang 212003 China|c|

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.

关键词： cancer gene expression data class imbalance paired differential expression genes decision ruleensemble learning majority voting

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