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Learning Valued Relations from Data

  • Conference paper
Eurofuse 2011

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 107))

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Abstract

Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated quite intensively in the machine learning community. To this end, current approaches typically consider datasets containing crisp relations, so that standard classification methods can be adopted. However, relations between objects like similarities and preferences are in many real-world applications often expressed in a graded manner. A general kernel-based framework for learning relations from data is introduced here. It extends existing approaches because both crisp and valued relations are considered, and it unifies existing approaches because different types of valued relations can be modeled, including symmetric and reciprocal relations. This framework establishes in this way important links between recent developments in fuzzy set theory and machine learning. Its usefulness is demonstrated on a case study in document retrieval.

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Author information

Authors and Affiliations

  1. KERMIT, Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, B-9000, Ghent, Belgium

    Willem Waegeman & Bernard De Baets

  2. Department of Information Technology and the Turku Centre for Computer Science, University of Turku, Joukahaisenkatu 3-5 B, 20520, Turku, Finland

    Tapio Pahikkala, Antti Airola & Tapio Salakoski

Authors
  1. Willem Waegeman
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  2. Tapio Pahikkala
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  3. Antti Airola
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  4. Tapio Salakoski
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  5. Bernard De Baets
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Editor information

Editors and Affiliations

  1. CITAB-UTAD University, Quinta dos Prados, 5000-911, Vila Real, Portugal

    Pedro Melo-Pinto , Pedro Couto  & Carlos Serôdio ,  & 

  2. Óbuda University, Bécsi út 96/B, H-1034, Budapest, Hungary

    János Fodor

  3. KERMIT, Research Unit Knowledge-based Systems, Universiteit Gent, Coupure links 653, 9000, Gent, Belgium

    Bernard De Baets

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© 2011 Springer-Verlag Berlin Heidelberg

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Waegeman, W., Pahikkala, T., Airola, A., Salakoski, T., De Baets, B. (2011). Learning Valued Relations from Data. In: Melo-Pinto, P., Couto, P., Serôdio, C., Fodor, J., De Baets, B. (eds) Eurofuse 2011. Advances in Intelligent and Soft Computing, vol 107. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24001-0_24

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  • DOI: https://doi.org/10.1007/978-3-642-24001-0_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24000-3

  • Online ISBN: 978-3-642-24001-0

  • eBook Packages: EngineeringEngineering (R0)

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