One Class Classifier Neural Network for Anomaly Detection in Low Dimensional Feature Spaces

In the last decade, many approaches have been developed to solve one-class classification (OCC) problems for anomaly detection. Many of them rely on estimating the statistical distribution of the data, find hidden patterns, or remap the data in advantageous feature spaces. This kind of techniques usually needs some a priori knowledge of the data distribution (i.e., Gaussian) or the setting of some parameters to achieve good classification performance, making their use less effective when the data distribution is unknown. In this paper, we propose a novel blind anomaly detection for low dimensional feature spaces, that exploits the flexibility of the neural network (NN) structure to find the class boundaries without any information about the shape of the data distribution. To prove the generality of the solution, we tested many different class shapes, and we applied it to a structural health monitoring (SHM) case study. Without requiring the tuning of hyperparameters, the performance of the proposed algorithm overcomes that of some known approaches like principal component analysis (PCA), kernel principal component analysis (KPCA), Gaussian mixture model (GMM), and autoassociative neural network (ANN) in many cases, and performs well in the specific SHM setting.