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- W2788395973 abstract "Learning a function f(X) that predicts Y from X is the archetypal Machine Learning (ML) problem. Typically, both sets of attributes (i.e., X,Y) have to be known before a model can be trained. When this is not the case, or when functions f(X) that predict Y from X are needed for varying X and Y, this may introduce significant overhead (separate learning runs for each function). In this paper, we explore the possibility of omitting the specification of X and Y at training time altogether, by learning a multi-directional, or versatile model, which will allow prediction of any Y from any X. Specifically, we introduce a decision tree-based paradigm that generalizes the well-known Random Forests approach to allow for multi-directionality. The result of these efforts is a novel method called MERCS: Multi-directional Ensembles of Regression and Classification treeS. Experiments show the viability of the approach." @default.
- W2788395973 created "2018-03-06" @default.
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- W2788395973 date "2018-04-29" @default.
- W2788395973 modified "2023-10-14" @default.
- W2788395973 title "MERCS: Multi-Directional Ensembles of Regression and Classification Trees" @default.
- W2788395973 doi "https://doi.org/10.1609/aaai.v32i1.11735" @default.
- W2788395973 hasPublicationYear "2018" @default.
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