Learning polynomial neural networks of a near-optimal connectivity for detecting abnormal patterns in biometric data

Existing Machine Learning (ML) approaches known from the literature require the user to set and experimentally adjust parameters of a decision model to achieve the best result. When artificial neural networks (ANNs) are employed, a typical problem is setting of a proper network structure and learning parameters that are required to minimise possible overfitting. We propose a new evolutionary strategy of learning an ANN structure of a near-optimal connectivity from the given data and show that such structures are less prone to overfitting. The resultant ANN consists of a reasonably small number of neurons that are concisely described by a set of short-term polynomial functions of variables that make a distinct contribution to the output. The proposed technique has been tested on the ML benchmarks and the results showed that the performance is comparable with that obtained by the conventional ML methods that require ad hoc tuning.