Incorporating Uncertainty via Hierarchical Classification Using Fuzzy Decision Trees

Object hierarchy is often ignored when collecting and classifying geographical objects. Object attributes are defined on the basis of uncertain parameters that may change in space and time. In this paper, we consider fuzzy decision trees for classification and a Bayesian hierarchical model for modeling and handling uncertainty. The study is illustrated with dynamic geographical objects from a coastal management application in the northern part of The Netherlands. Hierarchical modeling is applied to obtain posterior distributions for several boundary regions. The posterior distributions yield lower and upper limits of membership functions describing boundaries between object classes. In this way, a proper fuzzy decision tree for the coastal management application is built, which includes the inherent dynamic uncertainty.