Dealing with multi-source information in possibilistic logic
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Generalized Formal Concept Analysis
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Pattern Structures and Their Projections
ICCS '01 Proceedings of the 9th International Conference on Conceptual Structures: Broadening the Base
Reasoning in Inconsistent Stratified Knowledge Bases
ISMVL '96 Proceedings of the 26th International Symposium on Multiple-Valued Logic
Computational Linguistics
A framework for integrating information sources under lattice structure
Information Fusion
Two FCA-Based Methods for Mining Gene Expression Data
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
Possibilistic information fusion using maximal coherent subsets
IEEE Transactions on Fuzzy Systems
A possibility theory-oriented discussion of conceptual pattern structures
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
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The main problem addressed in this paper is the merging of numerical information provided by several sources (databases, experts...). Merging pieces of information into an interpretable and useful format is a tricky task even when an information fusion method is chosen. Fusion results may not be in suitable form for being used in decision analysis. This is generally due to the fact that information sources are heterogeneous and provide inconsistent information, which may lead to imprecise results. In this paper, we propose the use of Formal Concept Analysis and more specifically pattern structures for organizing the results of fusion methods. This allows us to associate any subset of sources with its information fusion result. Once a fusion operator is chosen, a concept lattice is built. With examples throughout this paper, we show that this concept lattice gives an interesting classification of fusion results. When the fusion global result is too imprecise, the method enables the users to identify what maximal subset of sources that would support a more precise and useful result. Instead of providing a unique fusion result, the method yields a structured view of partial results labelled by subsets of sources. Finally, an experiment on a real-world application has been carried out for decision aid in agricultural practices.