A Novel Deep Kernel Incremental Extreme Learning Machine Based on Artificial Transgender Longicorn Algorithm and Multiple Population Gray Wolf Optimization Methods

Abstract Redundant nodes in a kernel incremental extreme learning machine (KI-ELM) increase ineffective iterations and reduce learning efficiency. To address this problem, this study established a novel improved hybrid intelligent deep kernel incremental extreme learning machine (HI-DKIELM), which is based on a hybrid intelligent algorithm and a KI-ELM. First, a hybrid intelligent algorithm was established based on the artificial transgender longicorn algorithm and multiple population gray wolf optimization methods to reduce the parameters of hidden layer neurons and then to determine the effective number of hidden layer neurons. The learning efficiency of the algorithm was improved through the reduction of network complexity. Then, to improve the classification accuracy and generalization performance of the algorithm, a deep network structure was introduced to the KI-ELM to gradually extract the original input data layer by layer and realize high-dimensional mapping of data. The experimental results show that the number of network nodes of HI-DKIELM algorithm is obviously reduced, which reduces the network complexity of ELM and greatly improves the learning efficiency of the algorithm. From the regression and classification experiments, its CCPP can be seen that the training error and test error of the HI-DKIELM algorithm proposed in this paper are 0.0417 and 0.0435, which are 0.0103 and 0.0078 lower than the suboptimal algorithm, respectively. On the Boston Housing database, the average and standard deviation of this algorithm are 98.21 and 0.0038, which are 6.2 and 0.0003 higher than the suboptimal algorithm, respectively.