A constructive enhancement for Online Sequential Extreme Learning Machine

Abstract

Online Sequential Extreme Learning Machine (OS-ELM) proposed by Liang et al [1] is a faster and more accurate online sequential learning algorithm as compared to other current sequential algorithms. It can learn data one-by-one or chunk-by-chunk with fixed or varying chunk size. However, there is one of the remaining challenges for OS-ELM that it could not determine the optimal network structure automatically. In this paper, we propose a Constructive Enhancement for OS-ELM (CEOS-ELM), which can add random hidden nodes one-by-one or group-by-group with fixed or varying group size. CEOS-ELM is searching for the optimal network architecture during the sequential learning process, and it can handle both additive and radial basis function (RBF) hidden nodes. The optimal number of hidden nodes can be obtained automatically after training. The simulation results show that with CEOS-ELM, the network can achieve comparable generalization performance with OS-ELM and more compact network structure.