ELM-based Multiple Classifier Systems

With random weights between the inputs and the hidden units of three-layer feed-forward neural networks (namely extreme learning machine (ELM)), some favorable performance may be achieved for pattern classifications in terms of efficiency and effectiveness. This paper aims to investigate properties of ELM-based multiple classifier systems (MCS). A protein database with ten classes of super-families is employed in this study. Our results indicate that (1) integration of the base ELM classifiers with better learning performance may result in a MCS with better generalization power; (2) smaller size of weights in ELM classifiers does not imply a better generalization capability; and (3) under/over-fitting phenomena occurs for classification as inappropriate network architectures are used