Extreme Learning Tree

19 Dec 2019  ·  Anton Akusok, Emil Eirola, Kaj-Mikael Björk, Amaury Lendasse ·

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.

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