The features that characterize the onset of Huntington disease (HD) are poorly understood yet have significant implications for research and clinical practice. Motivated by the need to address this issue, and the fact that there may be inaccuracies in clinical HD data, we apply robust optimization and duality techniques to study support vector machine (SVM) classifiers in the face of uncertainty in feature data. We present readily numerically solvable semi-definite program reformulations via conic duality for a broad class of robust SVM classification problems under a general spectrahedron uncertainty set that covers the most commonly used uncertainty sets of robust optimization models, such as boxes, balls, and ellipsoids. In the case of the box-uncertainty model, we also provide a new simple quadratic program reformulation, via Lagrangian duality, leading to a very efficient iterative scheme for robust classifiers. Computational results on a range of datasets indicate that these robust classification methods allow for greater classification accuracies than conventional support vector machines in addition to selecting groups of highly correlated features. The conic duality-based robust SVMs were also successfully applied to a new, large HD dataset, achieving classification accuracies of over 95% and providing important information about the features that characterize HD onset.

人们很少了解表征亨廷顿病（HD）发作的特征，但对研究和临床实践具有重要意义。出于解决此问题的需要以及临床高清数据可能不准确的事实，我们面对特征数据的不确定性，应用了鲁棒的优化和对偶技术来研究支持向量机（SVM）分类器。在普通谱面不确定性集合下，我们涵盖了广泛的鲁棒SVM分类问题，我们通过圆锥对偶性提供了易于数值求解的半定式程序化，涵盖了鲁棒优化模型（例如盒，球和椭球）最常用的不确定性集合。对于盒不确定性模型，我们还提供了一种新的简单的二次程序重新表述，通过拉格朗日对偶，为鲁棒分类器提供了一种非常有效的迭代方案。一系列数据集上的计算结果表明，除了选择高度相关的特征组之外，这些鲁棒的分类方法比传统的支持向量机具有更高的分类精度。基于圆锥对偶性的鲁棒SVM也已成功应用于新的大型高清数据集，实现了超过95％的分类精度，并提供了有关高清发作特征的重要信息。一系列数据集上的计算结果表明，除了选择高度相关的特征组之外，这些鲁棒的分类方法比传统的支持向量机具有更高的分类精度。基于圆锥对偶性的鲁棒SVM也已成功应用于新的大型高清数据集，实现了超过95％的分类精度，并提供了有关高清发作特征的重要信息。一系列数据集上的计算结果表明，除了选择高度相关的特征组之外，这些鲁棒的分类方法比传统的支持向量机具有更高的分类精度。基于圆锥对偶性的鲁棒SVM也已成功应用于新的大型高清数据集，实现了超过95％的分类精度，并提供了有关高清发作特征的重要信息。