Pruned Annular Extreme Learning Machine Optimization Based on RANSAC Multi Model Response Regularization

Abstract

The accuracy and performance of machine learning and statistical models are still based on tuning some parameters and optimization for generating better predictive models of learning is based on training data. Larger datasets and samples are also problematic, due to increase in computational times, complexity and bad generalization due to outliers. Using the motivation from extreme learning machine (ELM), we proposed annular ELM based on RANSAC multi model response regularization to prune the large number of hidden nodes to acquire better optimality, generalization and classification accuracy of the network in ELM. Experimental results on different benchmark datasets showed that proposed algorithm optimally prunes the hidden nodes, better generalization and higher classification accuracy compared to other algorithms, including SVM, OP-ELM for binary and multi-class classification and regression problems.