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Managing Information Fusion with Formal Concept Analysis

Identifieur interne : 001707 ( Istex/Corpus ); précédent : 001706; suivant : 001708

Managing Information Fusion with Formal Concept Analysis

Auteurs : Zainab Assaghir ; Mehdi Kaytoue ; Amedeo Napoli ; Henri Prade

Source :

RBID : ISTEX:6315F3646BC4F0CCF03E0EDEE370B8DAAC668BAD

Abstract

Abstract: 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.

Url:
DOI: 10.1007/978-3-642-16292-3_12

Links to Exploration step

ISTEX:6315F3646BC4F0CCF03E0EDEE370B8DAAC668BAD

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<p>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.</p>
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<Para>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.</Para>
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<namePart type="given">Mehdi</namePart>
<namePart type="family">Kaytoue</namePart>
<affiliation>Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Campus Scientifique, B.P. 70239, 54500, Vandœuvre-lès-Nancy, France</affiliation>
<affiliation>E-mail: kaytouem@loria.fr</affiliation>
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<name type="personal">
<namePart type="given">Amedeo</namePart>
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<affiliation>Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Campus Scientifique, B.P. 70239, 54500, Vandœuvre-lès-Nancy, France</affiliation>
<affiliation>E-mail: napoli@loria.fr</affiliation>
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<name type="personal">
<namePart type="given">Henri</namePart>
<namePart type="family">Prade</namePart>
<affiliation>Institut de Recherche en Informatique de Toulouse (IRIT), 118 Route de Narbonne, 31062, Toulouse, France</affiliation>
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<abstract lang="en">Abstract: 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.</abstract>
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<title>Modeling Decisions for Artificial Intelligence</title>
<subTitle>7th International Conference, MDAI 2010, Perpignan, France, October 27-29, 2010. Proceedings</subTitle>
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<name type="personal">
<namePart type="given">Vicenç</namePart>
<namePart type="family">Torra</namePart>
<affiliation>IIIA-CSIC, Campus UAB s/n, 08193, Bellaterra, Catalonia, Spain</affiliation>
<affiliation>E-mail: vtorra@iiia.csic.es</affiliation>
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<name type="personal">
<namePart type="given">Yasuo</namePart>
<namePart type="family">Narukawa</namePart>
<affiliation>Toho Gakuen, 3-1-10 Naka, Kunitachi, 186-0004, Tokyo, Japan</affiliation>
<affiliation>E-mail: narukawa@d4.dion.ne.jp</affiliation>
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<namePart type="given">Marc</namePart>
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<affiliation>Université de Perpignan, Tecnosud, Rambla de la thermodynamique, 66100, Perpignan, France</affiliation>
<affiliation>E-mail: marc.daumas@univ-perp.fr</affiliation>
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<topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic>
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