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• W2865062175 abstract "We address the task of multi-target regression, where we generate global models that simultaneously predict multiple continuous variables. We use ensembles of generalized decision trees, called predictive clustering trees (PCTs), in particular bagging and random forests (RF) of PCTs and extremely randomized PCTs (extra PCTs). We add another dimension of randomization to these ensemble methods by learning individual base models that consider random subsets of target variables, while leaving the input space randomizations (in RF PCTs and extra PCTs) intact. Moreover, we propose a new ensemble prediction aggregation function, where the final ensemble prediction for a given target is influenced only by those base models that considered it during learning. An extensive experimental evaluation on a range of benchmark datasets has been conducted, where the extended ensemble methods were compared to the original ensemble methods, individual multi-target regression trees, and ensembles of single-target regression trees in terms of predictive performance, running times and model sizes. The results show that the proposed ensemble extension can yield better predictive performance, reduce learning time or both, without a considerable change in model size. The newly proposed aggregation function gives best results when used with extremely randomized PCTs. We also include a comparison with three competing methods, namely random linear target combinations and two variants of random projections." @default.
• W2865062175 created "2018-07-19" @default.
• W2865062175 creator A5004076545 @default.
• W2865062175 creator A5015674051 @default.
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• W2865062175 date "2018-07-11" @default.
• W2865062175 modified "2023-10-05" @default.
• W2865062175 title "Ensembles for multi-target regression with random output selections" @default.
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• W2865062175 doi "https://doi.org/10.1007/s10994-018-5744-y" @default.
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