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Learning valued relations from data

Willem Waegeman UGent, Tapio Pahikkala, Antti Airola, Tapio Salakoski and Bernard De Baets UGent (2011) Advances in Intelligent and Soft Computing. 107. p.257-268
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.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
CYCLE-TRANSITIVITY, RECIPROCAL RELATIONS, ROCK-PAPER-SCISSORS, PROMOTES BIODIVERSITY, PREFERENCES, FUZZY, BACTERIAL GAME
in
Advances in Intelligent and Soft Computing
editor
Pedro Melo-Pinto, Pedro Couto, Carlos Serôdio, János Fodor and Bernard De Baets UGent
volume
107
issue title
EUROFUSE 2011 : workshop on fuzzy methods for knowledge-based systems
pages
257 - 268
publisher
Springer
place of publication
Berlin, Germany
conference name
EUROFUSE Workshop on Fuzzy Methods for Knowledge-Based Systems
conference location
Peso da Régua, Portugal
conference start
2011-09-21
conference end
2011-09-23
Web of Science type
Proceedings Paper
Web of Science id
000303190600024
ISSN
1867-5662
ISBN
9783642240003
9783642240010
DOI
10.1007/978-3-642-24001-0_24
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2037301
handle
http://hdl.handle.net/1854/LU-2037301
date created
2012-02-17 14:14:00
date last changed
2015-12-17 10:20:49
@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},
}

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.