Abstract
In this work, we address the task of phenotypic traits prediction using methods for semi-supervised learning. More specifically, we propose to use supervised and semi-supervised classification trees as well as supervised and semi-supervised random forests of classification trees. We consider 114 datasets for different phenotypic traits referring to 997 microbial species. These datasets present a challenge for the existing machine learning methods: they are not labelled/annotated entirely and their distribution is typically imbalanced. We investigate whether approaching the task of phenotype prediction as a semi-supervised learning task can yield improved predictive performance. The results suggest that the semi-supervised methodology considered here is especially helpful when using single trees, especially when the amount of labeled data ranges from 20 to 40%. Similar improvements can be seen when the presence of the phenotype is very imbalanced.
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Notes
- 1.
Phenotype predictions from [7] are available at protraits.irb.hr.
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Acknowledgments
We acknowledge the financial support of the Slovenian Research Agency, via the grant P2-0103 and a young researcher grant to TSP, Croatian Science Foundation grants HRZZ-9623 (DescriptiveInduction), as well as the European Commission, via the grants ICT-2013-612944 MAESTRA and ICT-2013-604102 HBP. We would also like to acknowledge the joint support of the Republic of Slovenia and the European Union under the European Regional Development Fund (grant “Raziskovalci-2.0-FIŠ-52900”, implementation of the operation no. C3330-17-529008).
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Levatić, J. et al. (2018). Phenotype Prediction with Semi-supervised Classification Trees. In: Appice, A., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2017. Lecture Notes in Computer Science(), vol 10785. Springer, Cham. https://doi.org/10.1007/978-3-319-78680-3_10
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DOI: https://doi.org/10.1007/978-3-319-78680-3_10
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