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Exploring an Alternative Method for Handling Inconsistency in the Fusion of Expert and Learnt Information

  • Jonathan Rossiter
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 16)

Abstract

This paper presents an approach to reasoning with learnt and expert information where inconsistencies are present. Information is represented as an uncertain taxonomical hierarchy where each class is a concept specification either defined by an expert or learnt from data. We show through simple examples how learnt information and uncertain expert knowledge can be represented and how conclusions can be reasoned from the fused hierarchy. This reasoning mechanism relies on a default assumption to rank conclusions based on the position of contributing information in the class hierarchy. We examine the aggregation function of the default reasoning process and propose improvements that result in more natural fusions of expert and learnt hierarchical information.

Keywords

Expert Knowledge Aggregation Function Information Fusion Probability Interval Support Interval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Jonathan Rossiter
    • 1
  1. 1.AI Group, Dept. of Engineering MathematicsUniversity of BristolBristolUK

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