A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Conditional Ranking on Relational Data
Tekijät: Pahikkala T, Waegeman W, Airola A, Salakoski T, De Baets B
Toimittaja: Balcázar J, Bonchi F, Gionis A, Sebag M
Julkaisuvuosi: 2010
Journal: Lecture Notes in Computer Science
Kokoomateoksen nimi: Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part II
Tietokannassa oleva lehden nimi: MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT II
Lehden akronyymi: LECT NOTES ARTIF INT
Vuosikerta: 6322
Aloitussivu: 499
Lopetussivu: 514
Sivujen määrä: 16
ISSN: 0302-9743
Tiivistelmä
In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular target object. We present a general kernel framework for learning conditional rankings from various types of relational data, where rankings can be conditioned on unseen data objects. Conditional ranking from symmetric or reciprocal relations can in this framework be treated as two important special cases. Furthermore, we propose an efficient algorithm for conditional ranking by optimizing a squared ranking loss function. Experiments on synthetic and real-world data illustrate that such an approach delivers state-of-the-art performance in terms of predictive power and computational complexity. Moreover, we also show empirically that incorporating domain knowledge in the model about the underlying relations can improve the generalization performance.
In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular target object. We present a general kernel framework for learning conditional rankings from various types of relational data, where rankings can be conditioned on unseen data objects. Conditional ranking from symmetric or reciprocal relations can in this framework be treated as two important special cases. Furthermore, we propose an efficient algorithm for conditional ranking by optimizing a squared ranking loss function. Experiments on synthetic and real-world data illustrate that such an approach delivers state-of-the-art performance in terms of predictive power and computational complexity. Moreover, we also show empirically that incorporating domain knowledge in the model about the underlying relations can improve the generalization performance.
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