Ideal Centroid Striving: An Unsupervised and Prediction Parameterized Anomaly Detection Method

Automated machines are widely used in industrial environments for the production of different items and generate lots of data following these processes. The proper execution of the machines influences the production output, thus the detection of anomalous behavior in machines' activity using the generated data must be considered to avoid unpleasant outcomes. This paper presents a novel ensemble unsupervised anomaly detection method characterized by parameterized prediction. The proposed method consists of two stages - the first stage uses statistical-based methods to assign artificial labels to the input data. In the second stage, the artificially labeled instances and a feature bagging technique are employed to construct the model's estimators - each estimator calculates the percentiles of the distances computed between its centroid and each instance from the training subset. The model's prediction function is parameterized by a percentile rank. Each estimator computes the distance between its centroid and the evaluated instance: if this distance exceeds the value of the specified percentile, the estimator classifies the instance as an anomaly. A majority vote is applied to establish the outcome.