A fuzzy relation-based extension of Reggia's relational model for diagnosis handling uncertain and incomplete information
This work addresses diagnostic problems in fields like medicine or engineering by providing a more expressive representation for uncertain and incomplete data, though it is incremental as it builds on existing relational models.
The paper tackles the problem of handling uncertain and incomplete information in diagnostic models by proposing a new relational model based on possibility theory and twofold fuzzy sets, which extends Reggia's model to distinguish between absent and unobserved manifestations and between known and unknown causal relationships.
Relational models for diagnosis are based on a direct description of the association between disorders and manifestations. This type of model has been specially used and developed by Reggia and his co-workers in the late eighties as a basic starting point for approaching diagnosis problems. The paper proposes a new relational model which includes Reggia's model as a particular case and which allows for a more expressive representation of the observations and of the manifestations associated with disorders. The model distinguishes, i) between manifestations which are certainly absent and those which are not (yet) observed, and ii) between manifestations which cannot be caused by a given disorder and manifestations for which we do not know if they can or cannot be caused by this disorder. This new model, which can handle uncertainty in a non-probabilistic way, is based on possibility theory and so-called twofold fuzzy sets, previously introduced by the authors.