Graph Based Relational Features for Collective Classification

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CAO, Tru, ed. and others. Advances in Knowledge Discovery and Data Mining, 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015; Proceedings, Part II. Cham: Springer, 2015, pp. 447-458. Lecture Notes in Artificial Intelligence. 9078. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-18031-1. Available under: doi: 10.1007/978-3-319-18032-8_35
Zusammenfassung

Statistical Relational Learning (SRL) methods have shown that classification accuracy can be improved by integrating relations between samples. Techniques such as iterative classification or relaxation labeling achieve this by propagating information between related samples during the inference process. When only a few samples are labeled and connections between samples are sparse, collective inference methods have shown large improvements over standard feature-based ML methods. However, in contrast to feature based ML, collective inference methods require complex inference procedures and often depend on the strong assumption of label consistency among related samples. In this paper, we introduce new relational features for standard ML methods by extracting information from direct and indirect relations. We show empirically on three standard benchmark datasets that our relational features yield results comparable to collective inference methods. Finally we show that our proposal outperforms these methods when additional information is available.

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19th Pacific-Asia Conference, PAKDD 2015, 19. Mai 2015 - 22. Mai 2015, Ho Chi Minh City, Vietnam
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ISO 690BAYER, Immanuel, Uwe NAGEL, Steffen RENDLE, 2015. Graph Based Relational Features for Collective Classification. 19th Pacific-Asia Conference, PAKDD 2015. Ho Chi Minh City, Vietnam, 19. Mai 2015 - 22. Mai 2015. In: CAO, Tru, ed. and others. Advances in Knowledge Discovery and Data Mining, 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015; Proceedings, Part II. Cham: Springer, 2015, pp. 447-458. Lecture Notes in Artificial Intelligence. 9078. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-18031-1. Available under: doi: 10.1007/978-3-319-18032-8_35
BibTex
@inproceedings{Bayer2015-05-09Graph-39725,
  year={2015},
  doi={10.1007/978-3-319-18032-8_35},
  title={Graph Based Relational Features for Collective Classification},
  number={9078},
  isbn={978-3-319-18031-1},
  issn={0302-9743},
  publisher={Springer},
  address={Cham},
  series={Lecture Notes in Artificial Intelligence},
  booktitle={Advances in Knowledge Discovery and Data Mining, 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015; Proceedings, Part II},
  pages={447--458},
  editor={Cao, Tru},
  author={Bayer, Immanuel and Nagel, Uwe and Rendle, Steffen}
}
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