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Learning Valued Relations from Data

Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Bernard De Baets, Learning Valued Relations from Data. In: Pedro Melo-Pinto, Pedro Couto, Carlos Serôdio, János Fodor, Bernard De Baets (Eds.), Eurofuse 2011, Advances in Soft Computing 107, 257–268, Springer, 2012.

http://dx.doi.org/10.1007/978-3-642-24001-0_24

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.

BibTeX entry:

@INPROCEEDINGS{inpWaPaAiSaDe12a,
  title = {Learning Valued Relations from Data},
  booktitle = {Eurofuse 2011},
  author = {Waegeman, Willem and Pahikkala, Tapio and Airola, Antti and Salakoski, Tapio and De Baets, Bernard},
  volume = {107},
  series = {Advances in Soft Computing},
  editor = {Melo-Pinto, Pedro and Couto, Pedro and Serôdio, Carlos and Fodor, János and De Baets, Bernard},
  publisher = {Springer},
  pages = {257–268},
  year = {2012},
  keywords = {Machine Learning, Kernel Methods, Reciprocal Relations, Symmetric Relations},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI), Turku BioNLP Group

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