Reduction of the size of L-fuzzy contexts. A tool for differential diagnoses of diseases

Information extraction from an L-fuzzy context becomes a hard problem when we work with a large set of objects and/or attributes. The goal of this paper is to present two different and complementary techniques to reduce the size of the context. First, using overlap indexes, we will establish rankings among the elements of the context that will allow us to determine those that do not provide relevant information and eliminate them. Second, by means of Choquet integrals, we will aggregate some objects or attributes of the context in order to jointly use the provided information. One interesting application of the developed theory consists on helping in the differential diagnoses of diseases that share a large number of symptoms and, therefore, that are difficult of distinguish.This paper is partially supported by the Research Group 'Intelligent Systems and Energy (SI+E)' of the University of the Basque Country – UPV/EHU [grant number GIU 16/54] and by the Research Group 'Artificial Intelligence and Approximate Reasoning' of the Public University of Navarra [grant number TIN2016-77356-P]