To tackle the complex problem of providing business intelligence solutions based on business data, bioinspired deep learning has to be considered. This paper focuses on the application of artificial metaplasticity learning in business intelligence systems as an alternative paradigm of achieving a deeper information extraction and learning from arbitrary size data sets. As a case study, artificial metaplasticity multilayer perceptron applied to the automation of credit approval decision based on collected client data is analyzed, showing its potential and improvements over the state-of-the-art techniques. This paper successfully introduces the relevant novelty that the artificial neural network itself estimates the pdf of the input data to be used in the metaplasticity learning, so it is much closer to the biologic reality than previous implementations of artificial metaplasticity.

Artificial metaplasticity is the machine learning algorithm inspired in the biological metaplasticity of neural synapses. Metaplasticity stands for plasticity of plasticity, and as long as plasticity is related to memory, metaplasticity is related to learning. Implemented in supervised learning assuming input patterns distribution or a related function, it has proved to be very efficient in performance and in training convergence for multidisciplinary applications. Now, for the first time, this kind of artificial metaplasticity is implemented in an unsupervised neural network, achieving also excellent results that are presented in this paper. To compare results, a modified self-organization map is applied to the classification of MIT-BIH cardiac arrhythmias database.

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