Mass Assignment Methods for Medical Classification Diagnosis
Jim F. Baldwin, Carla Hill, Christiane Ponsan · Studies in fuzziness and soft computing · 2002
Nowadays, in areas such as medicine, many real-world classification problems rely heavily on large collections of data that are not understandable to human users. Therefore, there is a need for transparent models to represent such databases. In this chapter, we present two methods for learning classification rules which aim at being simplistic and transparent in nature. Both methods use fuzzy sets to describe the universes of discourse since their fuzzy boundaries allow a realistic representation of neighbouring concepts. As a consequence, interpolation effects as well as data compression are obtained in the learned models. Moreover, the fuzzy sets can be labelled with words which allows the inferred rules to be interpreted linguistically. In order to generate these rules, probability distributions need to be extracted from fuzzy sets, which is feasible using the fundamental results of mass assignment theory [2]. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.