Bayesian inference of Kansei knowledge by using variable precision rough sets
To discover uncertain and imprecision Kansei knowledge, a new approach on Kansei knowledge acquisition by using Bayesian Rough Sets model was introduced. Firstly, the knowledge rules were described through the Bayes network based on the general mapping framework of the Kansei knowledge representation. And then, the corresponding parameters of the Kansei knowledge rules were defined by variable precision rough set and the rules' reliability was calculated subsequently. So the Kansei knowledge, which used to support the conceptual design by the Kansei knowledge representation, reasoning and decision making, was further extracted and obtained effectively. Finally, an application about the form features of the cars' front view was presented.