A1 Refereed original research article in a scientific journal

A Kernel-Based Framework for Learning Graded Relations From Data




AuthorsWaegeman W, Pahikkala T, Airola A, Salakoski T, Stock M, De Baets B

PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Publication year2012

JournalIEEE Transactions on Fuzzy Systems

Journal name in sourceIEEE TRANSACTIONS ON FUZZY SYSTEMS

Journal acronymIEEE T FUZZY SYST

Number in series6

Volume20

Issue6

First page 1090

Last page1101

Number of pages12

ISSN1063-6706

DOIhttps://doi.org/10.1109/TFUZZ.2012.2194151(external)

Self-archived copy’s web addresshttps://research.utu.fi/converis/portal/detail/Publication/1319928(external)


Abstract
Driven by a large number of potential applications in areas, such as bioinformatics, information retrieval, and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated 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 often expressed in a graded manner in real-world applications. A general kernel-based framework for learning relations from data is introduced here. It extends existing approaches because both crisp and graded relations are considered, and it unifies existing approaches because different types of graded relations can be modeled, including symmetric and reciprocal relations. This framework establishes important links between recent developments in fuzzy set theory and machine learning. Its usefulness is demonstrated through various experiments on synthetic and real-world data. The results indicate that incorporating domain knowledge about relations improves the predictive performance.

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