Ensemble Based Extreme Learning Machine

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Abstract

Extreme learning machine (ELM) was proposed as a new class of learning algorithm for single-hidden layer feedforward neural network (SLFN). To achieve good generalization performance, ELM minimizes training error on the entire training data set, therefore it might suffer from overfitting as the learning model will approximate all training samples well. In this letter, an ensemble based ELM (EN-ELM) algorithm is proposed where ensemble learning and cross-validation are embedded into the training phase so as to alleviate the overtraining problem and enhance the predictive stability. Experimental results on several benchmark databases demonstrate that EN-ELM is robust and efficient for classification.

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