Decision trees as possibilistic classifiers

Concepts for Probabilistic and Possibilistic Induction of Decision Trees on Real World Data

The induction of decision trees from data is a well-known method for learning classifiers. The success of this method depends to a high degree on the measure used to select the next attribute, which, if tested, will improve the accuracy of the classification. This paper examines some possibility-based selection measures and compares them to probabilityand information-based measures on real worl...

On the Complexity of Decision Making in Possibilistic Decision Trees

When the information about uncertainty cannot be quantified in a simple, probabilistic way, the topic of possibilistic decision theory is often a natural one to consider. The development of possibilistic decision theory has lead to a series of possibilistic criteria, e.g pessimistic possibilistic qualitative utility, possibilistic likely dominance , binary possibilistic utility and possibilisti...

From Possibilistic Similarity Measures To Possibilistic Decision Trees

Lexicographic Refinements in Possibilistic Decision Trees

Possibilistic decision theory has been proposed twenty years ago and has had several extensions since then. Because of the lack of decision power of possibilistic decision theory, several refinements have then been proposed. Unfortunately, these refinements do not allow to circumvent the difficulty when the decision problem is sequential. In this article, we propose to extend lexicographic refi...

Open Archive TOULOUSE Archive Ouverte (OATAO)

Possibilistic decision theory has been proposed twenty years ago and has had several extensions since then. Because of the lack of decision power of possibilistic decision theory, several refinements have then been proposed. Unfortunately, these refinements do not allow to circumvent the difficulty when the decision problem is sequential. In this article, we propose to extend lexicographic refi...