微阵列数据分析中的自适应特征选择方法

Jie Cheng, J. Greshock, Leming Shi, Jeffery L. Painter, Xiwu Lin, Kwan R. Lee, Shu Zheng, R. Wooster, L. Pusztai, A. Menius
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引用次数: 2

摘要

特征选择是高维阵列数据分析中的重要研究课题之一。我们提出了一种基于双向过滤的方法,该方法利用一对统计数据加上严格的交叉验证,从不同类型的分布中识别出信息量最大的特征。我们评估了所提出的自适应特征选择方法在六个MicroArray质量控制阶段II (MAQC-II)数据集上的效用。结果表明,与其他特征选择方法产生的模型相比,我们的方法产生的模型特征明显减少,并且可以达到相当或更好的分类性能,表明我们的方法可以获得高质量的特征选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An adaptive feature selection method for microarray data analysis
Feature selection is one of the most important research topics in high dimensional array data analysis. We propose a two-way filtering based method that utilizes a pair of statistics coupled with rigorous cross-validation to identify the most informative features from different types of distributions. We evaluate the utility of the proposed adaptive feature selection method on six MicroArray Quality Control Phase II (MAQC-II) datasets. The results show that our method yields models with significantly fewer features and can achieve comparable or superior classification performance compared to models generated from other feature selection methods, suggesting high quality feature selection.
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