Abstract. A large experiment on combining classifiers is reported and discussed. It includes, both, the combination of different classifiers on the same feature set and the combination of classifiers on different feature sets. Various fixed and trained combining rules are used. It is shown

. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions—the sum rule—outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show

Abstract – In this paper one proposes a new simple combination rule, similar to Dempster’s rule, but the normalization is done for each set with respect to the non-zero sum of its corresponding mass matrix. A general formula is proposed with several numerical examples and comparisons with other

We survey existing approaches to the problem of combining rule languages with ontology languages for the Semantic Web. The focus is on the languages based on logic and on the reasoning in such languages. The objective is to give a uniform view of the approaches, and to outline related research

. Combining information from multiple sources is a topic of central importance for Uncertain Reasoning. In this paper we approach the problem of formulating appropriate Combination Rules for Uncertain Reasoning from an algebraic, lattice-theoretical perspective. Our purpose is to state the problem

In this article, we address the combination of non-independent sources to solve classification problems, within the theory of belief functions. We show that the cautious rule of combination [1, 2] is well-suited to such problems. We propose a method to learn the combination rule from training data

We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known

of the latent variables of each expert, because the combination rule ensures that the latent variables of different experts are conditionally independent when given the data. A product of experts (PoE) is therefore an interesting candidate for a perceptual system in which rapid inference is vital and generation

We show how to interoperate, semantically and inferentially, between the leading Semantic Web approaches to rules (RuleML Logic Programs) and ontologies (OWL/DAML+OIL Description Logic) via analyzing their expressive intersection. To do so, we define a new intermediate knowledge representation (KR

Abstract. The Carneades software system provides support for constructing, evaluating and visualizing arguments, using formal representations of facts, concepts, defeasible rules and argumentation schemes. This paper illustrates how rules and ontologies can be combined in Carneades with a prototype