Robust Classification via Support Vector Machines
This addresses the need for robust classifiers in machine learning applications where data uncertainty is a concern, but it is incremental as it builds on existing SVM frameworks.
The paper tackles the problem of classification models being sensitive to data uncertainty by constructing robust Support Vector Machine classifiers using two probabilistic methods: Single Perturbation for local feature uncertainty and Extreme Empirical Loss for aggregate uncertainty. The results show computational efficiency and advantages on synthetic and real-life data, though with possible limitations.
Classification models are very sensitive to data uncertainty, and finding robust classifiers that are less sensitive to data uncertainty has raised great interest in the machine learning literature. This paper aims to construct robust \emph{Support Vector Machine} classifiers under feature data uncertainty via two probabilistic arguments. The first classifier, \emph{Single Perturbation}, reduces the local effect of data uncertainty with respect to one given feature and acts as a local test that could confirm or refute the presence of significant data uncertainty for that particular feature. The second classifier, \emph{Extreme Empirical Loss}, aims to reduce the aggregate effect of data uncertainty with respect to all features, which is possible via a trade-off between the number of prediction model violations and the size of these violations. Both methodologies are computationally efficient and our extensive numerical investigation highlights the advantages and possible limitations of the two robust classifiers on synthetic and real-life data.