Spatio-temporal event data do not only arise from sensor readings, but also in information retrieval and text analysis. However, such events extracted from a text corpus may be imprecise in both dimensions. In this paper we focus on the task of event correlation, i.e., finding events that are similar in terms of space and time. We present a framework for Apache Spark that provides correlation o...

This paper presents solid assignment problem with imprecise costs. Robust’s ranking method is adopted for ranking the imprecise data. The fuzzy solid assignment problem has been transformed into crisp one and solved by plane point method. Numerical example is provided to illustrate the approach.

Data-based decision theory under imprecise probability has to deal with optimisation problems where direct solutions are often computationally intractable. Using the Γ-minimax optimality criterion, the computational effort may significantly be reduced in the presence of a least favorable model. In 1984, A. Buja derived a necessary and sufficient condition for the existence of a least favorable ...

In real-world problems, input data may be pervaded with uncertainty. In this paper, we investigate the behavior of naive possibilistic classifiers, as a counterpart to naive Bayesian ones, for dealing with classification tasks in presence of uncertainty. For this purpose, we extend possibilistic classifiers, which have been recently adapted to numerical data, in order to cope with uncertainty i...

The paper presents an efficient solution to decision problems where direct partial information on the distribution of the states of nature is available, either by observations of previous repetitions of the decision problem or by direct expert judgements. To process this information we use a recent generalization of Walley’s imprecise Dirichlet model, allowing us also to handle incomplete obser...

System modeling in dynamic environments needs processing of streams 1 of sensor data and incremental learning algorithms. This paper suggests an incre2 mental granular fuzzy rule-based modeling approach using streams of fuzzy inter3 val data. Incremental granular modeling is an adaptivemodeling framework that uses 4 fuzzy granular data that originate from unreliable sensors, imprecise perceptio...

The decision making problems in an imprecise environment has found paramount importance in recent years. Here we consider an object recognition problem in an imprecise environment. The recognition strategy is based on multiobserver input parameter data set. 2010 AMS Classification: 06D72

The paper studies the continuity of rules for updating imprecise probability models when new data are observed. Discontinuities can lead to robustness issues: this is the case for the usual updating rules of the theory of imprecise probabilities. An alternative, continuous updating rule is introduced.

We develop the first statistical matching micro approach reflecting the natural uncertainty arising during the integration of categorical data. A complete synthetic file is obtained by imprecise imputation, replacing missing entries by sets of suitable values. We discuss three imprecise imputation strategies and raise ideas on potential refinements by logical constraints or likelihood-based arg...

This article deals with an implementation of probist reliability problems in evidential networks to propagate imprecise probabilities expressed as fuzzy numbers. First, the problem of imprecise knowledge in reliability problems is described concerning system and data reprsentation. Then, the basics of the evidence theory and its use in a directed acyclic graph approach are given. The imprecise ...