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We have previously proposed an extended relational data model with the objective of supporting uncertain information in a consistent and coherent manner. The model, which can represent both uncertainty and imprecision in data, is based on the Dempster-Shafer (D-S) theory of evidence, and it uses bel and pls functions of the theory, with their definitions(More)
Experience shows that different text classification methods can give different results. We look here at a way of combining the results of two or more different classification methods using an evidential approach. The specific methods we have been experimenting with in our group include the support vector machine, kNN (nearest neighbors), kNN model-based(More)
The Dempster-Shafer theory gives a solid basis for reasoning appl icat ions characterized by uncertainty. A key feature of the theory is that proposi t ions are represented as subsets of a set which represents a hypothesis space. Th is power set along w i t h the set operations is a Boolean algebra. Can we generalize the theory to cover a rb i t ra ry(More)
Knowledge discovery approaches based on rough sets have successful application in machine learning and data mining. As these approaches are good at dealing with discrete values, a discretizer is required when the approaches are applied to continuous attributes. In this paper, a novel adaptive discretizer based on a statistical distribution index is proposed(More)