Extreme Learning Tree
This is an incremental improvement for machine learning practitioners seeking enhanced tree-based methods.
The paper tackles the problem of improving decision tree performance by introducing an Extreme Learning Tree, which combines an extremely random tree with non-linear data transformation and a linear observer, resulting in outperforming linear models on a benchmark dataset.
The paper proposes a new variant of a decision tree, called an Extreme Learning Tree. It consists of an extremely random tree with non-linear data transformation, and a linear observer that provides predictions based on the leaf index where the data samples fall. The proposed method outperforms linear models on a benchmark dataset, and may be a building block for a future variant of Random Forest.