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首页> 外文学位 >Feature selection methods for support vector machines for two or more classes, with applications to the analysis of Alzheimer's disease and its onset with MRI brain image processing.
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Feature selection methods for support vector machines for two or more classes, with applications to the analysis of Alzheimer's disease and its onset with MRI brain image processing.

机译：支持向量机的特征选择方法分为两类或更多类，可用于阿尔茨海默氏病的分析及其在MRI脑图像处理中的发作。

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摘要

Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature "markers". For support vector machine (SVM) classification, a widely used technique is Recursive Feature Elimination (RFE). We demonstrate RFE is not consistent with margin maximization, central to the SVM learning approach. We thus propose explicit margin-based feature elimination (MFE) for SVMs and show both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel, we show RFE assumes the squared weight vector 2-norm is strictly decreasing as features are eliminated. We demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. We show that MFE for nonlinear kernels gives better margin and generalization. We also present an extension which achieves further margin gains, by optimizing only two degrees of freedom---the hyperplane's intercept and its squared 2-norm---with the weight vector orientation fixed. We finally introduce an extension that allows margin slackness. We compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, UC Irvine repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results. We then develop several MFE-based feature elimination methods for the case of more than two classes (the "multiclass" case). We compare against RFE-based multiclass feature elimination and show that our MFE-based methods again consistently achieve better generalization performance. In summary, we identify some difficulties with the well-known RFE method, especially in the kernel case, develop novel, margin-based feature selection methods for linear and kernel-based two-class and multiclass discriminant functions for support vector machines (SVMs) addressing separable and nonseparable contexts, and provide an objective experimental comparison of several feature selection methods, which also evaluates consistency between a classifier's margin and its generalization accuracy.;We then apply our SVM classification and MFE methods to the challenging problem of predicting the onset of Alzheimer's Disease (AD), focusing on predicting conversion from Mild Cognitive Impairment (MCI) to AD using only a single, first-visit MRI for the person so as to aim for early diagnosis. In addition, we apply MFE for selecting brain regions as disease "biomarkers". For these aims, for the pre-classification image data preparation step, we co-develop an MRI brain image processing pipeline system named STAMPS, as well as develop a related system with additional capabilities named STAMPYS. These systems utilize external standard MRI brain image processing tools and generate output image types particularly suitable for detecting (and encoding) brain atrophy for Alzheimer's disease. We identify and remedy some basic MRI image processing problems caused by some limitations of external tools used in STAMPS---i.e. we introduce our basic fv (fill ventricle) algorithm for ventricle segmentation of cerebrospinal fluid (CSF). For prediction of conversion to AD for MCI patients, we demonstrate that our early diagnosis system achieves higher accuracy than similar recently published methods.
机译：在高维空间中进行分类的特征选择可以提高泛化能力，降低分类器的复杂度，并识别重要的，有区别的特征“标记”。对于支持向量机（SVM）分类，一种广泛使用的技术是递归特征消除（RFE）。我们证明，RFE与边际最大化不一致，这是SVM学习方法的核心。因此，与RFE相比，我们提出了针对SVM的显式基于余量的特征消除（MFE），并显示了改进的余量和改进的泛化能力。此外，对于非线性核的情况，我们显示RFE假设随着特征消除，平方加权向量2-范数严格减小。我们证明这对于高斯内核是不正确的，因此，在这种情况下，RFE可能会给出较差的结果。我们表明，非线性内核的MFE可以提供更好的余量和泛化。我们还提出了一种扩展，该扩展通过在权重矢量方向固定的情况下仅优化两个自由度（超平面的截距及其平方2范数）来获得更多的边际收益。最后，我们引入一种扩展，允许余量松弛。我们比较了几种替代方案，包括RFE和将特征选择嵌入分类器设计的线性编程方法。在高维基因微阵列数据集，UC Irvine储存库数据集和阿尔茨海默氏病脑图像数据上，MFE方法给出了可喜的结果。然后，我们针对两个以上类的情况（“多类”情况）开发了几种基于MFE的特征消除方法。我们将其与基于RFE的多类特征消除进行了比较，并表明我们基于MFE的方法再次始终如一地实现了更好的泛化性能。总而言之，我们发现了众所周知的RFE方法的一些困难，特别是在内核情况下，针对支持向量机（SVM）的线性和基于内核的两类和多类判别函数，开发了新颖的，基于余量的特征选择方法解决可分离和不可分离的上下文，并提供几种特征选择方法的客观实验比较，还评估了分类器的余量及其泛化精度之间的一致性。;然后，我们将SVM分类和MFE方法应用于预测发病的挑战性问题阿尔茨海默氏病（AD），专注于仅使用该患者的一次初诊MRI来预测从轻度认知障碍（MCI）到AD的转化，以便于早期诊断。此外，我们将MFE应用于选择脑部区域作为疾病“生物标志物”。为了实现这些目标，在预分类图像数据准备步骤中，我们共同开发了名为STAMPS的MRI脑图像处理管线系统，并开发了具有附加功能的相关系统STAMPYS。这些系统利用外部标准MRI脑图像处理工具，并生成特别适合检测（和编码）阿尔茨海默氏病脑萎缩的输出图像类型。我们确定并补救了一些基本的MRI图像处理问题，这些问题是由STAMPS中使用的外部工具的某些局限性引起的-我们介绍了用于脑脊髓液（CSF）的脑室分割的基本fv（填充脑室）算法。为了预测MCI患者向AD的转化，我们证明了我们的早期诊断系统比最近发表的类似方法具有更高的准确性。

著录项

  • 作者

    Aksu, Yaman.;

  • 作者单位

    The Pennsylvania State University.;

  • 授予单位 The Pennsylvania State University.;
  • 学科 Engineering Electronics and Electrical.;Health Sciences Radiology.;Computer Science.;Engineering Biomedical.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 101 p.
  • 总页数 101
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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