  Extreme learning machine (ELM) as a neural network algorithm has shown its
good performance, such as fast speed, simple structure etc, but also, weak
robustness is an unavoidable defect in original ELM for blended data. We
present a new machine learning framework called LARSEN-ELM for overcoming this
problem. In our paper, we would like to show two key steps in LARSEN-ELM. In
the first step, preprocessing, we select the input variables highly related to
the output using least angle regression (LARS). In the second step, training,
we employ Genetic Algorithm (GA) based selective ensemble and original ELM. In
the experiments, we apply a sum of two sines and four datasets from UCI
repository to verify the robustness of our approach. The experimental results
show that compared with original ELM and other methods such as OP-ELM,
GASEN-ELM and LSBoost, LARSEN-ELM significantly improve robustness performance
while keeping a relatively high speed.
