Robust generalized eigenvalue classifier with ellipsoidal uncertainty
Robust generalized eigenvalue classifier with ellipsoidal uncertainty
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DOI：
10.1007/s10479-012-1303-2
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发表时间：
2014-05
影响因子：
4.8
通讯作者：
P. Xanthopoulos;M. Guarracino;P. Pardalos
中科院分区：
文献类型：
--
作者：
P. Xanthopoulos;M. Guarracino;P. Pardalos
不确定性是一个与数据采集和分析相关的概念，通常以噪声或测量误差的形式出现，通常是由于某些技术限制。在监督学习中，不确定性影响分类精度，产生低质量的解决方案。因此，开发能够有效处理不精确数据的机器学习算法至关重要。本文从鲁棒优化的角度研究这一问题。我们考虑了一种基于广义特征值的监督学习算法，并在椭球不确定性集的情况下提供了一个鲁棒的对应公式和解。我们在来自加州大学欧文分校（UCI）机器学习存储库的人工和基准数据集上展示了所提出的鲁棒方案的性能，并将结果与支持向量机的鲁棒实现进行了比较。
Uncertainty is a concept associated with data acquisition and analysis, usually appearing in the form of noise or measure error, often due to some technological constraint. In supervised learning, uncertainty affects classification accuracy and yields low quality solutions. For this reason, it is essential to develop machine learning algorithms able to handle efficiently data with imprecision. In this paper we study this problem from a robust optimization perspective. We consider a supervised learning algorithm based on generalized eigenvalues and we provide a robust counterpart formulation and solution in case of ellipsoidal uncertainty sets. We demonstrate the performance of the proposed robust scheme on artificial and benchmark datasets from University of California Irvine (UCI) machine learning repository and we compare results against a robust implementation of Support Vector Machines.