Hua Jianping, Lowey James, Xiong Zixiang, Dougherty Edward R
Computational Biology Division, Translational Genomics Research Institute, Phoenix, USA.
BMC Bioinformatics. 2006 May 31;7:274. doi: 10.1186/1471-2105-7-274.
Overfitting the data is a salient issue for classifier design in small-sample settings. This is why selecting a classifier from a constrained family of classifiers, ones that do not possess the potential to too finely partition the feature space, is typically preferable. But overfitting is not merely a consequence of the classifier family; it is highly dependent on the classification rule used to design a classifier from the sample data. Thus, it is possible to consider families that are rather complex but for which there are classification rules that perform well for small samples. Such classification rules can be advantageous because they facilitate satisfactory classification when the class-conditional distributions are not easily separated and the sample is not large. Here we consider neural networks, from the perspectives of classical design based solely on the sample data and from noise-injection-based design.
This paper provides an extensive simulation-based comparative study of noise-injected neural-network design. It considers a number of different feature-label models across various small sample sizes using varying amounts of noise injection. Besides comparing noise-injected neural-network design to classical neural-network design, the paper compares it to a number of other classification rules. Our particular interest is with the use of microarray data for expression-based classification for diagnosis and prognosis. To that end, we consider noise-injected neural-network design as it relates to a study of survivability of breast cancer patients.
The conclusion is that in many instances noise-injected neural network design is superior to the other tested methods, and in almost all cases it does not perform substantially worse than the best of the other methods. Since the amount of noise injected is consequential, the effect of differing amounts of injected noise must be considered.
在小样本情况下，数据过拟合是分类器设计中的一个突出问题。这就是为什么通常更倾向于从受约束的分类器族中选择分类器，这些分类器没有过度精细划分特征空间的可能性。但过拟合不仅仅是分类器族的结果；它高度依赖于用于从样本数据设计分类器的分类规则。因此，有可能考虑一些相当复杂的分类器族，但存在适用于小样本的分类规则。这样的分类规则可能是有利的，因为当类条件分布不容易分离且样本量不大时，它们有助于实现令人满意的分类。在这里，我们从仅基于样本数据的经典设计以及基于噪声注入的设计这两个角度来考虑神经网络。
本文提供了一项基于广泛模拟的噪声注入神经网络设计的比较研究。它考虑了各种小样本量下的多种不同特征 - 标签模型，并使用了不同量的噪声注入。除了将噪声注入神经网络设计与经典神经网络设计进行比较外，本文还将其与其他一些分类规则进行了比较。我们特别感兴趣的是使用微阵列数据进行基于表达的诊断和预后分类。为此，我们考虑噪声注入神经网络设计与乳腺癌患者生存能力研究的关系。
结论是，在许多情况下，噪声注入神经网络设计优于其他测试方法，并且在几乎所有情况下，其表现都不会比其他最佳方法差太多。由于注入的噪声量很重要，所以必须考虑不同注入噪声量的影响。
