Evolutionary Ordinal Extreme Learning Machine

Recently the ordinal extreme learning machine (ELMOR) algorithm has been proposed to adapt the extreme learning machine (ELM) algorithm to ordinal regression problems (problems where there is an order arrangement between categories). In addition, the ELM standard model has the drawback of needing many hidden layer nodes in order to achieve suitable performance. For this reason, several alternatives have been proposed, such as the evolutionary extreme learning machine (EELM). In this article we present an evolutionary ELMOR that improves the performance of ELMOR and EELM for ordinal regression. The model is integrated in the differential evolution algorithm of EELM, and it is extended to allow the use of a continuous weighted RMSE fitness function which is proposed to guide the optimization process. This favors classifiers which predict labels as close as possible (in the ordinal scale) to the real one. The experiments include eight datasets, five methods and three specific performance metrics. The results show the performance improvement of this type of neural networks for specific metrics which consider both the magnitude of errors and class imbalance.