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Learning valued relations from data

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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.
Keywords
CYCLE-TRANSITIVITY, RECIPROCAL RELATIONS, ROCK-PAPER-SCISSORS, PROMOTES BIODIVERSITY, PREFERENCES, FUZZY, BACTERIAL GAME

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Chicago
Waegeman, Willem, Tapio Pahikkala, Antti Airola, Tapio Salakoski, and Bernard De Baets. 2011. “Learning Valued Relations from Data.” In Advances in Intelligent and Soft Computing, ed. Pedro Melo-Pinto, Pedro Couto, Carlos Serôdio, János Fodor, and Bernard De Baets, 107:257–268. Berlin, Germany: Springer.
APA
Waegeman, W., Pahikkala, T., Airola, A., Salakoski, T., & De Baets, B. (2011). Learning valued relations from data. In P. Melo-Pinto, P. Couto, C. Serôdio, J. Fodor, & B. De Baets (Eds.), Advances in Intelligent and Soft Computing (Vol. 107, pp. 257–268). Presented at the EUROFUSE Workshop on Fuzzy Methods for Knowledge-Based Systems, Berlin, Germany: Springer.
Vancouver
1.
Waegeman W, Pahikkala T, Airola A, Salakoski T, De Baets B. Learning valued relations from data. In: Melo-Pinto P, Couto P, Serôdio C, Fodor J, De Baets B, editors. Advances in Intelligent and Soft Computing. Berlin, Germany: Springer; 2011. p. 257–68.
MLA
Waegeman, Willem, Tapio Pahikkala, Antti Airola, et al. “Learning Valued Relations from Data.” Advances in Intelligent and Soft Computing. Ed. Pedro Melo-Pinto et al. Vol. 107. Berlin, Germany: Springer, 2011. 257–268. Print.
@inproceedings{2037301,
  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.},
  author       = {Waegeman, Willem and Pahikkala, Tapio and Airola, Antti and Salakoski, Tapio and De Baets, Bernard},
  booktitle    = {Advances in Intelligent and Soft Computing},
  editor       = {Melo-Pinto, Pedro and Couto, Pedro and Ser{\^o}dio, Carlos and Fodor, J{\'a}nos and De Baets, Bernard},
  isbn         = {9783642240003},
  issn         = {1867-5662},
  keyword      = {CYCLE-TRANSITIVITY,RECIPROCAL RELATIONS,ROCK-PAPER-SCISSORS,PROMOTES BIODIVERSITY,PREFERENCES,FUZZY,BACTERIAL GAME},
  language     = {eng},
  location     = {Peso da R{\'e}gua, Portugal},
  pages        = {257--268},
  publisher    = {Springer},
  title        = {Learning valued relations from data},
  url          = {http://dx.doi.org/10.1007/978-3-642-24001-0\_24},
  volume       = {107},
  year         = {2011},
}

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