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In real world applications, data are often uncertain or imperfect. In most classification approaches, they are transformed into precise data. However, this uncertainty is an information in itself which should be part of the learning process. Data uncertainty can take several forms: probabilities, (fuzzy)sets of possible values, expert assessments, etc. We therefore need a flexible and generic enough...
In decision tasks, imprecision and uncertainty can arise from many sources such as data uncertainty, data reliability, or the necessity to use intermediate (non-fully reliable) models. User preferences may also be included into the decision process, making it even more complex. This is particularly true for processes involving biological materials. Soft computing methods have the potential to be the...
Many authors have studied fuzzy belief structures, that is belief functions having fuzzy sets as focal elements. One of the main reason for this is that this structure offers a convenient way to mix probabilistic and fuzzy information. Still, one point on which authors often disagree is how information represented by fuzzy belief structures should be processed, i.e., how should be defined fusion operations,...
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