Random forests with random projections of the output space for high dimensional multi-label classification

Arnaud Joly; Pierre Geurts; Louis Wehenkel

doi:10.1007/978-3-662-44848-9_39

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arXiv · 1404.3581

Random forests with random projections of the output space for high dimensional multi-label classification

Arnaud Joly·Pierre Geurts·Louis Wehenkel

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Abstract

We adapt the idea of random projections applied to the output space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without affecting computational complexity and accuracy of predictions. We also show that random output space projections may be used in order to reach different bias-variance tradeoffs, over a broad panel of benchmark problems, and that this may lead to improved accuracy while reducing significantly the computational burden of the learning stage.

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BibTeX RIS

Arnaud Joly, Pierre Geurts, Louis Wehenkel. 2014-04-14. Random forests with random projections of the output space for high dimensional multi-label classification. https://doi.org/10.1007/978-3-662-44848-9_39

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