This paper provides a supervised classification tree-based methodology to deal with Multivalued data, specifically predictors measurements can be provided by a functional distribution or an interval of values. Main literature refers to symbolic data analysis, aiming to extend standard methods such as factorial analysis, clustering, discriminant analysis, etc., to deal with symbolic data tables....

The selection of best Information System (IS) project from many competing proposals is a critical business activity which is very helpful to all organizations. While previous IS project selection methods are useful but have restricted application because they handle only cases with precise data. Indeed, these methods are based on precise data with less emphasis on imprecise data. This paper pro...

Until recently, local governments in Spain were using machines with rolling cylinders for testing and verification of taximeters. However, the tyres condition can lead to errors in the process and the mechanical construction of the test equipment is not compatible with certain vehicles. Thus, a new measurement device should be designed. In our opinion, the verification of a taximeter will not b...

In this paper we present our approach for extending the OLAP model to include treatment of value uncertainty as part of a multidimensional model inhabited by flexible data and non-rigid hierarchical structures of organisation. A new multidimensional-cubic model named as the IF-Cube is introduced which is able to operate over data with imprecision either in the facts or in the dimensional hierar...

The key step in the construction of the Delaunay triangulation of a finite set of planar points is to establish correctly whether a given point of this set is inside or outside the circle determined by any other three points. We address the problem of formulating the in-circle test when the coordinates of the planar points are given only up to a given precision, which is usually the case in pra...

We propose a new methodology to classify temporal data with imprecise hidden Markov models. For each sequence we learn a different model by coupling the EM algorithm with the imprecise Dirichlet model. As a model descriptor, we consider the expected value of the observable variable in the limit of stationarity of the Markov chain. In the imprecise case, only the bounds of this descriptor can be...

We introduce a robust regression method for imprecise data, and apply it to social survey data. Our method combines nonparametric likelihood inference with imprecise probability, so that only very weak assumptions are needed and different kinds of uncertainty can be taken into account. The proposed regression method is based on interval dominance: interval estimates of quantiles of the error di...

Graphical models—especially probabilistic networks like Bayes networks and Markov networks—are very popular to make reasoning in highdimensional domains feasible. Since constructing them manually can be tedious and time consuming, a large part of recent research has been devoted to learning them from data. However, if the dataset to learn from contains imprecise information in the form of sets ...