Reinforced Extreme Learning Machines for Fast Robust Regression in the Presence of Outliers
Benoît Frénay, Michel Verleysen · IEEE Transactions on Cybernetics · 2015
Extreme learning machines (ELMs) are fast methods that obtain state-of-the-art results in regression. However, they are not robust to outliers and their meta-parameter (i.e., the number of neurons for standard ELMs and the regularization constant of output weights for L2 -regularized ELMs) selection is biased by such instances. This paper proposes a new robust inference algorithm for ELMs which is based on the pointwise probability reinforcement methodology. Experiments show that the proposed approach produces results which are comparable to the state of the art, while being often faster.