Immune-inspired extreme learning machine: Performance evaluation and conceptual analyses

Abstract

Recently, it was proposed a novel hybrid approach to train MLPs which combines the advantages of a powerful artificial immune system, called GAIS, with the advantages of Extreme Learning Machine (ELM). In that proposal, the GAIS algorithm is responsible for finding a proper set of input weights whereas the output weights are determined by the Moore-Penrose generalized inverse. The methodology was evaluated only in classification problems and its performance compares favorably with that presented by state-of-the-art-algorithms. Motivated by this scenario, this paper better formalizes the proposal and performs a deeper investigation of its usefulness for synthesizing MLP and RBF neural networks on several real-world classification and regression problems. The computational experiments have shown that the proposed methodology outperforms other approaches in both quantitative and qualitative aspects.