Explaining and interpreting hyperdimensional computing classifiers on tabular data

Abstract

Given the rise in the usage of artificial intelligence models and machine learning approaches in our day-to-day lives, it has become increasingly important to explain these models to increase user trust. Hyperdimensional Computing (HDC) has been introduced as a powerful, energy-efficient algorithmic framework that is intrinsically less opaque than (deep) neural networks. Nevertheless, the possibility of explaining and interpreting the HDCbased classification model has not yet been explored explicitly. Therefore, this work proposes an explanation method and an interpretation method for the HDC-based classification model working with tabular data. The proposed methods have been successfully evaluated on three tabular data sets with a diverse number of samples, features, and classes. Their faithfulness is validated with coherence checks, the deletion and insertion metrics, and a feature ablation study. The results of the proposed explanation method align well with the well-studied LIME explanations.