Learning Sparse-Lets for Interpretable Classification of Event-interval Sequences

Event-interval sequences are defined as multivariate series of events that occur over time. The classification of event-interval sequences has gained increasing attention among researchers in the field of time series analysis due to their broad applicability, as for instance in healthcare and weather forecasting. This paper focuses on the optimized extraction of interpretable features from event-interval sequences to construct supervised classifiers. The current state-of-the-art is represented by e-lets, which are randomly sampled subsequences of event-intervals. We propose a new approach to interpretable classification of event-interval sequences based on sparse-lets, a novel generalization of e-lets. Our approach relies on genetic algorithms to learn sparse-lets, generating optimized and interpretable features. We evaluate the performance of our method through experiments conducted on benchmark datasets, and compare it against the state-of-the-art. Computational results show that our method is a viable competitor in terms of classification accuracy. Moreover, we show that our method generates simpler features than competing approaches, retaining only the most important information.