Cabalar, PedroMartín, RodrigoMuñiz, BraisPérez, Gilberto2020-10-272020-10-272020-08-28Cabalar, P.; Martín, R.; Muñiz, B.; Pérez, G. aspBEEF: Explaining Predictions Through Optimal Clustering . Proceedings 2020, 54, 51. https://doi.org/10.3390/proceedings20200540512504-3900http://hdl.handle.net/2183/26555[Abstract] In this paper we introduce aspBEEF, a tool for generating explanations for the outcome of an arbitrary machine learning classifier. This is done using Grover’s et al. framework known as Balanced English Explanations of Forecasts (BEEF) that generates explanations in terms of in terms of finite intervals over the values of the input features. Since the problem of obtaining an optimal BEEF explanation has been proved to be NP-complete, BEEF existing implementation computes an approximation. In this work we use instead an encoding into the Answer Set Programming paradigm, specialized in solving NP problems, to guarantee that the computed solutions are optimal.engAtribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/Knowledge representationAnswer set programmingExplainable AIconference outputopen access10.3390/proceedings2020054051