Evolutionary Extreme Learning Machine and Its Application to Image Analysis

Extreme learning machine (ELM) and evolutionary ELM (E-ELM) were proposed as a new class of learning algorithm for single-hidden layer feedforward neural network (SLFN). In order to achieve good generalization performance, E-ELM calculates the error on a subset of testing data for parameter optimization. Since E-ELMemploys extra data for validation to avoid the overfitting problem, more samples are needed for model training. In this paper, the cross-validation strategy is proposed to be embedded into the training phase so as to solve the overtraining problem. Based on this new learning structure, two extensions of E-ELM are introduced. Experimental results demonstrate that the proposed algorithms are efficient for image analysis.