Improvements for P-ELM1 and P-ELM2 Pruning Algorithms in Extreme Learning Machines

Abstract

Extreme learning machine (ELM) is an efficient training algorithm for single-hidden layer feed-forward neural networks (SLFNs). Two pruned-ELM named P-ELM1 and P-ELM2 are proposed by Rong et al. P-ELM1 and P-ELM2 employ [Formula: see text] and information gain to measure the association between the class labels and individual hidden node respectively. But for the continuous value data sets, it is inevitable for P-ELM1 and P-ELM2 to evaluate the probability distributions of the data sets with discretization methods for calculating [Formula: see text] and information gain, while the discretization will lead to information loss. Furthermore, the discretization will result in high computational complexity. In order to deal with the problems, based on tolerance rough sets, this paper proposed an improved pruned-ELM algorithm, which can overcome the drawbacks mentioned above. Experimental results along with statistical analysis on 8 UCI data sets show that the improved algorithm outperforms the pruned-ELM in computational complexity and testing accuracy.