Algorithms for Multi-criteria optimization in Possibilistic Decision Trees

We also present an approximation algorithm of a tree by a self-nested one that can be used in fast prediction of edit distance between two trees..

We propose a novel way to model aware- ness with partial valuations that drops public signature awareness and can model agent signature unawareness, and we give a first view

We then searched for contextual information within asso- ciated documentation and data, before carefully studying the initial data used in the analysis process and identifying

to be a simple way to overcome the poor adhesive properties of PU without the need of supplementary surface treatments. We characterize the obtained films in terms of

Using this selection criterion (the subset criterion), the test chosen for the root of the decision tree uses the attribute and subset of its values that maximizes the

A not member and not connected node which detects that a child wants to be connected (see Predicate Asked Connection) changes its variable need to true. which be- longs to the

In addition, when weighted time series split the data in a node, a weight vector visual rendering improves the interpretability of the model.. However, we did not obtain impact on

A large number of extensions of the decision tree algorithm have been proposed for overfitting avoidance, handling missing attributes, handling numerical attributes, etc..