DNA微阵列数据集成分类研究进展

T. Khoshgoftaar, D. Dittman, Randall Wald, Wael Awada
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引用次数: 15

摘要

集成分类是近年来研究的热点,特别是在生物信息学领域。集成分类的好处(不容易过度拟合,提高分类性能,减少偏差)是困扰生物信息学实验的许多问题的完美匹配。对于DNA微阵列数据实验来说尤其如此,因为数据量大(每个样本可能有数万个基因探针的结果),而且数据中固有的噪音很大。本文综述了DNA微阵列集成分类应用的研究现状。我们讨论了迄今为止的研究已经证明了什么,并确定了需要进行更多研究的领域。
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A Review of Ensemble Classification for DNA Microarrays Data
Ensemble classification has been a frequent topic of research in recent years, especially in bioinformatics. The benefits of ensemble classification (less prone to overfitting, increased classification performance, and reduced bias) are a perfect match for a number of issues that plague bioinformatics experiments. This is especially true for DNA microarray data experiments, due to the large amount of data (results from potentially tens of thousands of gene probes per sample) and large levels of noise inherent in the data. This work is a review of the current state of research regarding the applications of ensemble classification for DNA microarrays. We discuss what research thus far has demonstrated, as well as identify the areas where more research is required.
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