2023-05-242023-05-242011-06Álvarez, M.Á., González Abril, L., Ortega Ramírez, J.A., Soria Morillo, L.M. y Cuberos Gallardo, F.J. (2011). A quantitative methodology to identify related features in data sets. En XIII Jornadas de ARCA: Sistemas Cualitativos y sus Aplicaciones en Diagnosis, Robótica e Inteligencia Ambiental (JARCA 2011) (39-43), Huelva, España: Universidad de Sevilla.978-84-615-5513-0https://hdl.handle.net/11441/146581In this paper, a methodology which quantifies the dependence beteen features in a data set is developed. This methodology uses the Ameva discretization algorithm. In particular, it uses the Ameva coefficient to quantify the dependece. Furthermore, a new coefficient called entropy has been proposed for cases where it is not possible to apply the Ameva discretization algorithm. Thus, different matrices of inter-dependence are built provinding a grade of dependence between two features. Finally, to verify the qualitiews of this methodology, a simple method to discard features base don it is applied to a well-known data set in a classification process and promising results for the carried out system are obtained.application/pdf5engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess