Nguyen Tat Thanh College, University of Industry, Ho Chi Minh City, Vietnam.
Adv Exp Med Biol. 2011;696:135-43. doi: 10.1007/978-1-4419-7046-6_14.
The classification of biological samples measured by DNA microarrays has been a major topic of interest in the last decade, and several approaches to this topic have been investigated. However, till now, classifying the high-dimensional data of microarrays still presents a challenge to researchers. In this chapter, we focus on evaluating the performance of the training algorithms of the single hidden layer feedforward neural networks (SLFNs) to classify DNA microarrays. The training algorithms consist of backpropagation (BP), extreme learning machine (ELM) and regularized least squares ELM (RLS-ELM), and an effective algorithm called neural-SVD has recently been proposed. We also compare the performance of the neural network approaches with popular classifiers such as support vector machine (SVM), principle component analysis (PCA) and fisher discriminant analysis (FDA).
在过去的十年中，通过 DNA 微阵列测量的生物样本分类一直是一个主要研究课题，并且已经研究了几种针对该主题的方法。但是，到目前为止，对微阵列的高维数据进行分类仍然是研究人员面临的挑战。在本章中，我们专注于评估单隐藏层前馈神经网络 (SLFNs) 的训练算法对 DNA 微阵列进行分类的性能。训练算法包括反向传播 (BP)、极限学习机 (ELM) 和正则化最小二乘 ELM (RLS-ELM)，最近还提出了一种称为神经-SVD 的有效算法。我们还将神经网络方法的性能与支持向量机 (SVM)、主成分分析 (PCA) 和 Fisher 判别分析 (FDA) 等流行的分类器进行了比较。
