Self-adaptive Extreme Learning Machine Optimized by Rough Set Theory and Affinity Propagation Clustering

Abstract

Recently, a simple and efficient learning algorithm for single hidden layer feedforward networks (SLFNs) called extreme learning machine (ELM) has been developed by G.-B. Huang et al. One key strength of ELM algorithm is that there is only one parameter, the number of hidden nodes, to be determined while it has the significantly low computational time required for training new classifiers and good generalization performance. However, there is no effective method for finding the proper and universal number of hidden nodes. In order to address this problem, we propose a self-adaptive extreme learning machine (SELM) algorithm. SELM algorithm determines self-adaptively the number of hidden nodes and constructs Gaussian function as activation functions of hidden nodes. And in this algorithm, rough set theory acts as the pre-treatment cell to eliminate the redundant attributes of data sets. Then, affinity propagation clustering (AP Clustering) is used to self-adaptively determine the number of hidden nodes, while the centers and widths of AP clustering are utilized to construct a Gaussian function in the hidden layer of SLFNs. Empirical study of SELM algorithm on several commonly used classification benchmark problems shows that the proposed algorithm can find the proper number of hidden nodes and construct compact network classifiers, comparing with traditional ELM algorithm.