In cooperation with the data mining group of the research center Ulm of Daimler-Benz AG we work on methods for knowledge discovery in data bases.
Decision trees are a widely used method to classify objects due to their features.
Decision trees are induced from sets of classified data. In classical methods, measures from probability or information theory are used to select relevant features. However, it is also possible to use possibility theory. We extended the well known decision tree learning algorithm C4.5 with possibilistic measures. Our group works on the problem of combining conflicting and partially inconsistent information that comes from different sources of different reliability.