Hybrid Structure-Adaptive RBF-ELM Network Classifier

Abstract

In this paper, a hybrid structure-adaptive radial basis function-extreme learning machine (HSARBF-ELM) network classifier is presented. HSARBF-ELM consists of a structure-adaptive radial basis function (SARBF) network and an extreme learning machine (ELM) network of cascade, where the output of the SARBF network hidden layer is used as the input layer of the ELM network. In the HSARBF-ELM network classifier, the SARBF network is utilized to achieve adaptively localizing kernel mapping of input vectors, after that step, the ELM network is utilized to implement global classification of mapping samples in the kernel space. HSARBF-ELM indicates the combination of localized kernel mapping learning and the global nonlinear classification, which combines the advantages of the SARBF network and the ELM network. The quantitative conditions for the separability enhancement and the corresponding theoretical explanation for the HSARBF-ELM network are given, which demonstrate that when input vectors go through the SARBF network, adaptively adjusting the RBF kernel parameters can boost the separability of the original sample space. Thus, the classification performance of the HSARBF-ELM network can be guaranteed theoretically. An appropriate learning algorithm for the HSARBF-ELM network is subsequently presented, which effectively combines the methods of density clustering with a potential function, center-oriented unidirectional repulsive force and the existing ELM algorithm, and the optimized complementary HSARBF-ELM network can be constructed. The experimental results show that the classification performance of the HSARBF-ELM network clearly outperforms the ELM network, and outperforms other classifiers on most classification problems.