v-Anomica: A Fast Support Vector-Based Novelty Detection Technique

Outlier or anomaly detection refers to the task of identifying abnormal or inconsistent patterns from a dataset. While they may seem to be undesirable entities, identifying them has many potential applications in fraud and intrusion detection, medical research, and safety-critical vehicle health management. Outliers can be detected using supervised, semi-supervised, or unsupervised techniques. Unsupervised techniques do not require labeled instances for detecting outliers. Supervised techniques require labeled instances of both normal and abnormal operation data for first building a model (e.g., a classifier), and then testing if an unknown data point is a normal one or an outlier. The model can be probabilistic such as Bayesian inference or deterministic such as decision trees, Support Vector Machines (SVMs), and neural networks. Semi-supervised techniques only require labeled instances of normal data. Hence, they are more widely applicable than the fully supervised ones. These techniques build models of normal data and then flag outliers that do not fit the model.