In order to solve the problem, this paper proposes a hybrid model which combines multiple feature selection models to select the most significant input features from all potentially relevant features.
These typically leverage the techniques learned from statistical machine learning, coupled with ensemble architectures that create multiple feature detection models.
Unlike current modelling systems, multiple-view feature modelling can adequately support this, by providing an own view on a product for each phase.
We define three types of multiple feature interpretations or models, (1) only positive, (2) mixed and (3) only negative.
To achieve this, a new modelling system has been developed, which uses enhanced multiple-view feature modelling.
The general concept of this multiple-view feature modelling approach, the functionality of the four views, and the way the views are kept consistent, are described.
An approach to multiple-view feature modelling is presented that supports conceptual design, assembly design, part detail design and part manufacturing planning.
It does not only provide views with form features to model single parts, as previous approaches to multiple-view feature modelling did, but also a view with conceptual features, to model the product configuration with functional components and interfaces between these components, and a view with assembly features, to model the connections between components.
Not only did the Rag & Bone men's show feature models with multiple earrings, but young designers like Joseph Altuzarra and older ones like Marc Jacobs are back into studs.
