This paper describes a novel methodology for predicting fault prone modules. The methodology is based on Dempster-Shafer (D-S) belief networks. Our approach consists of three steps: First, building the Dempster-Shafer network by the induction algorithm; Second, selecting the predictors (attributes) by the logistic procedure; Third, feeding the predictors describing the modules of the current pr...

How to extend standard support vector machines to solve multi-class classification problem and yield the outputs in the frame of Dempster-Shafer theory is useful. The multi-class probability support vector machine is proposed, firstly. The Dempster-Shafer theory based multi-class support vector machine is designed by constructing probability support vector machines for binary classification usi...

This work proposes a new method of fusion image using Dempster-Shafer theory and local variability (DST-LV). takes into account the behaviour each pixel with its neighbours. It consists in calculating quadratic distance between value I (x, y) point all neighbouring pixels. Local is used to determine mass function defined DempsterShafer theory. The two classes studied are : fuzzy part focused pa...

Given a number of Dempster-Shafer belief functions there are different architectures which allow to do a compilation of the given knowledge. These architectures are the Shenoy-Shafer Architecture, the Lauritzen-Spiegelhalter Architecture and the HUGIN Architecture. We propose a new architecture called ”Fast-Division Architecture” which is similar to the former two. But there are two important a...

In evidential clustering, uncertainty about the assignment of objects to clusters is represented by Dempster-Shafer mass functions. The resulting clustering structure, called a credal partition, is shown to be more general than hard, fuzzy, possibilistic and rough partitions, which are recovered as special cases. Three algorithms to generate a credal partition are reviewed. Each of these algori...

We are concerned with formal models of reasoning under uncertainty. Many approaches to this problem are known in the literature e.g. Dempster-Shafer theory, bayesian-based reasoning, belief networks, fuzzy logics etc. We propose rough mere-ology as a foundation for approximate reasoning about complex objects. Our notion of a complex object includes approximate proofs understood as schemes const...

Introducing features of natural language into query languages for databases increases their power and ergo-nomics. Therefore, theories are needed which formalise these features of natural language. Focusing on inde-terminateness we discuss some existing theories, especially fuzzy logics and Dempster-Shafer-Theory, in order to develop a paradigm which allows for a direct comparison of diierent t...

Fuzzy sets and rough sets are known as uncertainty models. They are proposed to treat different aspects of uncertainty. Therefore, it is natural to combine them to build more powerful mathematical tools for treating problems under uncertainty. In this chapter, we describe the state of the art in the combinations of fuzzy and rough sets dividing into three parts. In the first part, we first desc...

One method for modelling uncertain or inaccurate information is rough set analysis which was introduced and studied by Pawlak (1982) and his co–workers. Unlike other methods such as fuzzy set theory, Dempster – Shafer theory or statistical methods, rough set analysis requires no external parameters and uses only the information presented in the given data. In the present study we apply rough se...