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Utilizing Hierarchies in Tree-Based Online Structured Output Prediction

  • Aljaž OsojnikEmail author
  • Panče Panov
  • Sašo Džeroski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11828)

Abstract

Methods for online prediction of structured values are becoming more and more popular. However, hierarchical prediction, which has recently been shown to produce good results in terms of predictive performance in the batch learning setting, has not yet been applied in the online learning setting. We address the recently introduced task of hierarchical multi-target regression. To this end, we propose a hierarchical extension of iSOUP-Tree, which can address online multi-target regression. The extension weighs the split evaluation heuristic according to the location of the targets in the hierarchy. We design the experimental setup to ascertain whether the additional information contained in the hierarchy can be utilized to improve the predictive performance in the leaf targets. The proposed method shows promising results, producing potential improvements that should be investigated further.

Keywords

Online hierarchical prediction Hierarchical multi-target regression 

Notes

Acknowledgements

This work is supported by grants funded by the Slovenian Research Agency (P2-0103, N2-0056, J2-9230) and grants funded by the European Commission (H2020 785907 HBP SGA2, H2020 635201 LANDMARK, H2020 769661 SAAM, H2020 833671 RESILOC).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Knowledge TechnologiesJožef Stefan InstituteLjubljanaSlovenia
  2. 2.Jožef Stefan International Postgraduate SchoolLjubljanaSlovenia

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