Random forests with random projections of the output space for high dimensional multi-label classification
This work addresses efficiency and accuracy challenges in multi-label classification for domains with high-dimensional outputs, presenting an incremental improvement over existing tree-based methods.
The paper tackles high-dimensional multi-label classification by adapting random projections of the output space to tree-based ensemble methods, resulting in reduced learning time complexity and improved accuracy across benchmark problems without affecting computational complexity or prediction accuracy.
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.