no code implementations • 28 Dec 2021 • Erfan Aasi, Mingyu Cai, Cristian Ioan Vasile, Calin Belta
In this paper, we introduce a time-incremental learning framework: given a dataset of labeled signal traces with a common time horizon, we propose a method to predict the label of a signal that is received incrementally over time, referred to as prefix signal.
1 code implementation • 1 Oct 2021 • Erfan Aasi, Cristian Ioan Vasile, Mahroo Bahreinian, Calin Belta
Our algorithm leverages an ensemble of Concise Decision Trees (CDTs) to improve the classification performance, where each CDT is a decision tree that is empowered by a set of techniques to generate simpler formulae and improve interpretability.
no code implementations • 24 May 2021 • Erfan Aasi, Cristian Ioan Vasile, Mahroo Bahreinian, Calin Belta
Many autonomous systems, such as robots and self-driving cars, involve real-time decision making in complex environments, and require prediction of future outcomes from limited data.