Abstract
Declarative Modelling is an early-phase design technique allowing the user to describe an object or an environment in abstract terms, closer to human intuition. The geometric solutions automatically yielded for such a description are evaluated by the user and may be subsequently used for the construction of a computational model of his/her preferences. Due to the physical limitations of the human evaluator, and the large number of the representations produced, only a subset of the latter are actually evaluated by the user and eventually a small number of them are approved, leading to imbalanced datasets in regard to the learning mechanism invoked. In the current work we discuss and assess the capability of a mechanism adopted for user modelling in a declarative design environment to handle this imbalance. The experimental results in this context indicate considerable efficiency in the prediction for the under-represented class.
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Bardis, G., Miaoulis, G., Plemenos, D. (2009). 8 User Profiling from Imbalanced Data in a Declarative Scene Modelling Environment. In: Plemenos, D., Miaoulis, G. (eds) Artificial Intelligence Techniques for Computer Graphics. Studies in Computational Intelligence, vol 159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85128-8_8
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DOI: https://doi.org/10.1007/978-3-540-85128-8_8
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