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.
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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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Osojnik, A., Panov, P., Džeroski, S. (2016). Comparison of Tree-Based Methods for Multi-target Regression on Data Streams. In: Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2015. Lecture Notes in Computer Science(), vol 9607. Springer, Cham. https://doi.org/10.1007/978-3-319-39315-5_2
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DOI: https://doi.org/10.1007/978-3-319-39315-5_2
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