Extreme Learning Machine with Harmony Search for High Dimensional Data Classifications

Extreme learning machine (ELM) with harmony search (HS) method for solving high dimensional data pattern classifications (abbreviated to HDHS-ELM) is studied in this paper. Firstly, HDHS mechanism which can enhance the global search ability to make features in feature space more separable is used to adjust the hidden nodes' parameters of neural classifier to improve the classification performance and reduce the hidden neurons' quantity. For balancing and reducing empirical and structural risks, the output weights are then computed through regularization technique. Compared with classifiers trained with another three algorithms related to ELM, the simulation results of classifier trained with HDHS- ELM illustrate better performance.