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Correlation kernels for support vector machines classification with applications in cancer data

机译：Correlation kernels for support vector machines classification with applications in cancer data

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摘要

High dimensional bioinformatics data sets provide an excellent and challenging research problem in machine learning area. In particular, DNA microarrays generated gene expression data are of high dimension with significant level of noise. Supervised kernel learning with an SVM classifier was successfully applied in biomedical diagnosis such as discriminating different kinds of tumor tissues. Correlation Kernel has been recently applied to classification problems with Support Vector Machines (SVMs). In this paper, we develop a novel and parsimonious positive semidefinite kernel. The proposed kernel is shown experimentally to have better performance when compared to the usual correlation kernel. In addition, we propose a new kernel based on the correlation matrix incorporating techniques dealing with indefinite kernel. The resulting kernel is shown to be positive semidefinite and it exhibits superior performance to the two kernels mentioned above. We then apply the proposed method to some cancer data in discriminating different tumor tissues, providing information for diagnosis of diseases. Numerical experiments indicate that our method outperforms the existing methods such as the decision tree method and KNN method.
机译：高维生物信息学数据集在机器学习领域提供了一个极好的且具有挑战性的研究问题。特别地，DNA微阵列产生的基因表达数据是高维的，具有明显的噪声水平。使用SVM分类器的监督核学习已成功应用于生物医学诊断，例如区分不同种类的肿瘤组织。相关内核最近已应用于支持向量机（SVM）的分类问题。在本文中，我们开发了一个新颖且简约的正半定核。与普通的相关内核相比，实验证明了所提出的内核具有更好的性能。另外，我们提出了一种基于相关矩阵的新内核，该内核结合了处理不确定内核的技术。所得的内核显示为正半定值，并且表现出比上述两个内核更高的性能。然后，我们将提出的方法应用于区分不同肿瘤组织的某些癌症数据，为疾病诊断提供信息。数值实验表明，我们的方法优于现有的决策树方法和KNN方法。

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  • 作者

    Jiang H; Ching WK;

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  • 年度 2012
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  • 原文格式 PDF
  • 正文语种 eng
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