Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals

When machine learning is applied in safety-critical or otherwise sensitive areas, the analysis of feature relevance can be an important tool to keep the size of models small, and thus easier to understand, and to analyze how different features impact the behavior of the model. In the presence of correlated features, feature relevances and the solution to the minimal-optimal feature selection problem are not unique. One approach to solving this problem is identifying feature relevance intervals that symbolize the range of relevance given to each feature by a set of equivalent models. In this contribution, we address the issue of calculating relevance intervals – a unique representation of relevance – for reject option support vector machines with a linear kernel, which have the option of rejecting a data point if they are unsure about its label.