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Performance Comparison of SLFN Training Algorithms for DNA Microarray Classification

  • Hieu Trung Huynh
  • Jung-Ja Kim
  • Yonggwan Won
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 696)

Abstract

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).

Keywords

Support Vector Machine Hide Layer Single Value Decomposition Extreme Learning Machine Hide Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Nguyen Tat Thanh CollegeUniversity of IndustryHo Chi Minh CityVietnam

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