A comprehensive evaluation of random vector functional link networks

With randomly generated weights between input and hidden layers, a random vector functional link network is a universal approximator for continuous functions on compact sets with fast learning property. Though it was proposed two decades ago, the classification ability of this family of networks has not been fully investigated yet. Through a very comprehensive evaluation by using 121 UCI datasets, the effect of bias in the output layer, direct links from the input layer to the output layer and type of activation functions in the hidden layer, scaling of parameter randomization as well as the solution procedure for the output weights are investigated in this work. Surprisingly, we found that the direct link plays an important performance enhancing role in RVFL, while the bias term in the output neuron had no significant effect. The ridge regression based closed-form solution was better than those with Moore-Penrose pseudoinverse. Instead of using a uniform randomization in [−1,+1] for all datasets, tuning the scaling of the uniform randomization range for each dataset enhances the overall performance. Six commonly used activation functions were investigated in this work and we found that hardlim and sign activation functions degenerate the overall performance. These basic conclusions can serve as general guidelines for designing RVFL networks based classifiers.