TY - JOUR N2 - Abstract The paper presents a study on data-driven diagnostic rules, which are easy to interpret by human experts. To this end, the Dempster-Shafer theory extended for fuzzy focal elements is used. Premises of the rules (fuzzy focal elements) are provided by membership functions which shapes are changing according to input symptoms. The main aim of the present study is to evaluate common membership function shapes and to introduce a rule elimination algorithm. Proposed methods are first illustrated with the popular Iris data set. Next experiments with five medical benchmark databases are performed. Results of the experiments show that various membership function shapes provide different inference efficiency but the extracted rule sets are close to each other. Thus indications for determining rules with possible heuristic interpretation can be formulated. L1 - http://www.czasopisma.pan.pl/Content/104496/PDF/acsc-2016-0022.pdf L2 - http://www.czasopisma.pan.pl/Content/104496 PY - 2016 IS - No 3 DO - 10.1515/acsc-2016-0022 A1 - Porębski, Sebastian A1 - Straszecka, Ewa PB - Committee of Automatic Control and Robotics PAS DA - 2016 T1 - Membership Functions for Fuzzy Focal Elements UR - http://www.czasopisma.pan.pl/dlibra/publication/edition/104496 T2 - Archives of Control Sciences ER -