Data-agnostic pivotal instances selection for decision-making models

As decision-making processes become increasingly complex, machine learning tools have become essential resources for tackling business and social issues. However, many methodologies rely on complex models that experts and everyday users cannot really interpret or understand. This is why constructing interpretable models is crucial. Humans typically make decisions by comparing the case at hand with a few exemplary and representative cases imprinted in their minds. Our objective is to design an approach that can select such exemplary cases, which we call pivots, to build an interpretable predictive model. To this aim, we propose a hierarchical and interpretable pivot selection model inspired by Decision Trees, and based on the similarity between pivots and input instances. Such a model can be used both as a pivot selection method, and as a standalone predictive model. By design, our proposal can be applied to any data type, as we can exploit pre-trained networks for data transformation. Through experiments on various datasets of tabular data, texts, images, and time series, we have demonstrated the superiority of our proposal compared to naive alternatives and state-of-the-art instance selectors, while minimizing the model complexity, i.e., the number of pivots identified.