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

LARSEN-ELM: Selective Ensemble of Extreme Learning Machines using LARS for Blended Data