Extreme Learning Tree

Anton Akusok 1, Emil Eirola 1, Kaj-Mikael Bj ork 2 Amaury Lendasse 3, 4 1 Arcada University of Applied Sciences, Helsinki, Finland 2 Risklab at Arcada UAS, Helsinki, Finland 3 Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, USA 4 The Iowa Informatics Initiative, The University of Iowa, Iowa City, USA Abstract The paper proposes a new variant of a decision tree, called an Extreme Learning Tree. It consists of an extremely random tree with nonlinear 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. 1 Introduction Randomized methods are a recent trend in practical machine learning [1]. They enable the high performance of complex nonlinear methods without the high computational cost of their optimization. Current most prominent examples are randomized neural networks, in both feed-forward [2] and recurrent [3] forms. For the latter, the randomized approach provided an efficient training method for the first time, and enabled achieving state-of-the-art performance in multiple areas [4].