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Comparison of Tree-Based Methods for Multi-target Regression on Data Streams

  • Aljaž Osojnik
  • Panče Panov
  • Sašo Džeroski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9607)

Abstract

Single-target regression is a classical data mining task that is popular both in the batch and in the streaming setting. Multi-target regression is an extension of the single-target regression task, in which multiple continuous targets have to be predicted together. Recent studies in the batch setting have shown that global approaches, predicting all of the targets at once, tend to outperform local approaches, predicting each target separately. In this paper, we explore how different local and global tree-based approaches for multi-target regression compare in the streaming setting. Specifically, we apply a local method based on the FIMT-DD algorithm and propose a novel global method, named iSOUP-Tree-MTR. Furthermore, we present an experimental evaluation that is mainly oriented towards exploring the differences between the local and the global approach.

Keywords

Data Stream Predictive Performance Target Variable Global Approach Memory Consumption 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors are supported by The Slovenian Research Agency (Grant P2-0103 and a young researcher grant) and the European Commission (Grants ICT-2013-612944 MAESTRA and ICT-2013-604102 HBP).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Aljaž Osojnik
    • 1
    • 2
  • Panče Panov
    • 1
  • Sašo Džeroski
    • 1
    • 2
    • 3
  1. 1.Jožef Stefan InstituteLjubljanaSlovenia
  2. 2.Jožef Stefan International Postgraduate SchoolLjubljanaSlovenia
  3. 3.Centre of Excellence for Integrated Approaches in Chemistry and Biology of ProteinsLjubljanaSlovenia

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