Online Cluster Drift Detection for Novelty Detection in Data Streams
Shon Mendelson, Boaz Lerner · 2020
A major challenge in data stream applications is the change in the target variable over time in unexpected ways, a phenomenon called concept drift (CD). Another challenge is the emergence of novel classes, soliciting novelty detection (ND) by, e.g., one-class or semi-supervised classification. But, in online ND, these two challenges interfere with each other although they should be dealt with jointly. We present the cluster drift detection (CDD) algorithm that, using a single hyper-parameter, performs offline clustering to learn the diverse normal profile, and detects online whether a never-seen-before example is novel or normal using a multivariate statistical test. If it is normal, the CDD uses this example to update the normal-profile cluster, enabling continuous CD monitoring. Experimental results using popular real-world and synthetic data sets, as well as a precision agriculture data set of banana plants under water stress and a COVID-19 data set demonstrate that the CDD algorithm: 1) distinguishes between normal and novel concepts more accurately than state-of-the-art algorithms, 2) provides information about why specific novel concepts are misdetected, and 3) is more robust to the complexity, drift, and noise in the problem than other algorithms.