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Communication Dans Un Congrès Année : 2006

Imprecise regression and regression on fuzzy data A preliminary discussion

Résumé

The paper provides a discussion of the possibilistic regression method originally proposed by H. Tanaka. This method has the advantage of allowing the learning of an imprecise model, in the form of an interval-valued function. It may lead to an imprecise model even in presence of precise data, which is satisfactory from a learning point of view. Indeed, finding a precise model that perfectly represents the concept to be learned is illusory, due to the existence of the bias caused by the choice of a modeling representation space, the limited amount of data, and the possibility of missing relevant data. However, what is obtained with possibilistic regression is more an imprecise model than a genuine fuzzy one. The paper illustrates and emphasizes this point on environmental data and suggest two different approaches for learning genuine fuzzy regression models from precise data
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Dates et versions

hal-03364221 , version 1 (05-10-2021)

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Mathieu Serrurier, Henri Prade. Imprecise regression and regression on fuzzy data A preliminary discussion. 2006 IEEE International Conference on Fuzzy Systems, Jul 2006, Vancouver, Canada. ⟨10.1109/FUZZY.2006.1681908⟩. ⟨hal-03364221⟩
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