An Optimal Approach for Pruning Annular Regularized Extreme Learning Machines

Larger datasets, with many samples are problematic for solving problems in data mining and machine learning, due to increase in computational times, increased complexity, and bad generalization due to outliers. Further, the accuracy and performance of machine learning and statistical models are still based on tuning of some parameters and optimizing them for generating better predictive models of learning. In this paper, we propose a novel formulation of Extreme Learning Machines - the Annular ELM, with RANSAC multi model response regularization for pruning large number of hidden nodes to acquire better optimality, generalization and classification accuracy. Experimental evaluation of the proposed ELM formulation on different benchmark datasets showed that the algorithm optimally prunes the hidden nodes, with better generalization and higher classification accuracy as compared to other algorithms, including the well-known SVM, OP-ELM for binary and multi-class classification and regression problems. Also, we extended the proposed algorithm to a more complex application context involving MRI Brain Image classification. For this study, we examine the performance of the proposed algorithm on magnetic resonance images (MRI) of various states of brain by extracting the most significant features, and to classify them into normal and abnormal brain images.