Dynamic adjustment of hidden layer structure for convex incremental extreme learning machine

Extreme Learning Machine (ELM) is a learning algorithm based on generalized single-hidden-layer feed-forward neural network. Since ELM has an excellent performance on regression and classification problems, it has been paid more and more attention recently. The determination of structure of ELM plays a vital role in ELM applications. Essentially, determination of the structure of ELM is equivalent to the determination of the hidden layer structure. Utilizing a smaller scale of the hidden layer structure can promote faster running speed. In this paper, we propose algorithm PCI-ELM (Pruned-Convex Incremental Extreme Learning Machine) based on CI-ELM (Convex Incremental Extreme Learning Machine). Furthermore, we also present an improved PCI-ELM algorithm, EPCI-ELM (Enhanced Pruned-Convex Incremental Extreme Learning Machine), which introduces a filtering strategy for PCI-ELM during the neurons adding process. In order to adjust the single-hidden-layer feed-forward neural network more flexibly and achieve the most compact form of the hidden layer structure, in this paper, we propose an algorithm which can dynamically determine hidden layer structure, DCI-ELM (Dynamic Convex Incremental Extreme Learning Machine). At the end of this paper, we verify the performance of PCI-ELM, EPCI-ELM and DCI-ELM. The results show that PCI-ELM, EPCI-ELM and DCI-ELM control hidden layer structure very well and construct the more compact single-hidden-layer feed-forward neural network.