Fast Explanation of RBF-Kernel SVM Models Using Activation Patterns

Machine learning models have significantly enriched the toolbox in the field of neuroimaging analysis. Among them, Support Vector Machines (SVM) have been one of the most popular models for supervised learning, but their use primarily relies on linear SVM models due to their explainability. Kernel SVM models are capable classifiers but more opaque. Recent advances in eXplainable AI (XAI) have developed several feature importance methods to address the explainability problem. However, noise variables can affect these explanations, making irrelevant variables regarded as important variables. This problem also appears in explaining linear models, which the linear pattern can address. This paper proposes a fast method to explain RBF kernel SVM globally by adopting the notion of linear pattern in kernel space. Our method can generate global explanations with low computational cost and is less affected by noise variables. We successfully evaluate our method on simulated and real MEG/EEG datasets.