Training the neural networks by electromagnetism-like mechanism based algorithm

Recently, medical data mining has become one of the most popular topics in the data mining community. This is due to the societal importance of the field and also the particular computational challenges posed in this domain of data mining. Early researches concentrated on sequential heuristics and later moved to meta-heuristic approaches due to the ability of these approaches to generate better solutions. The aim of this paper is to introduce the basic principles of a new meta-heuristic algorithm called Electromagnetism-like Mechanism (EMag) for neural network training. EMag simulates the electromagnetism theory of physics by considering each data sample to be an electrical charge. For neural network, EMag simulates the attraction-repulsion mechanism of each weight connection as charge partials to move towards the optimum without being trapped into local minimum. The performance of the proposed algorithm is evaluated in 12 of benchmark classification problems, and the computational results show that the proposed algorithm performs better than the standard back propagation algorithm.