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Found 8 papers, 1 papers with code
From global to local MDI variable importances for random forests and when they are Shapley values
Random forests have been widely used for their ability to provide so-called importance measures, which give insight at a global (per dataset) level on the relevance of input variables to predict a certain output.
Gradient tree boosting with random output projections for multi-label classification and multi-output regression
We consider several extensions of gradient boosting to address such problems.
Random Subspace with Trees for Feature Selection Under Memory Constraints
Dealing with datasets of very high dimension is a major challenge in machine learning.
Unit Commitment using Nearest Neighbor as a Short-Term Proxy
We devise the Unit Commitment Nearest Neighbor (UCNN) algorithm to be used as a proxy for quickly approximating outcomes of short-term decisions, to make tractable hierarchical long-term assessment and planning for large power systems.
Context-dependent feature analysis with random forests
In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output.
Classifying pairs with trees for supervised biological network inference
Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference.
Random forests with random projections of the output space for high dimensional multi-label classification
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.
Understanding variable importances in forests of randomized trees
Despite growing interest and practical use in various scientific areas, variable importances derived from tree-based ensemble methods are not well understood from a theoretical point of view.
