  Uncertain changes in data streams present challenges for machine learning
models to dynamically adapt and uphold performance in real-time. Particularly,
classification boundary change, also known as real concept drift, is the major
cause of classification performance deterioration. However, accurately
detecting real concept drift remains challenging because the theoretical
foundations of existing drift detection methods - two-sample distribution tests
and monitoring classification error rate, both suffer from inherent limitations
such as the inability to distinguish virtual drift (changes not affecting the
classification boundary, will introduce unnecessary model maintenance), limited
statistical power, or high computational cost. Furthermore, no existing
detection method can provide information on the trend of the drift, which could
be invaluable for model maintenance. This work presents a novel real concept
drift detection method based on Neighbor-Searching Discrepancy, a new statistic
that measures the classification boundary difference between two samples. The
proposed method is able to detect real concept drift with high accuracy while
ignoring virtual drift. It can also indicate the direction of the
classification boundary change by identifying the invasion or retreat of a
certain class, which is also an indicator of separability change between
classes. A comprehensive evaluation of 11 experiments is conducted, including
empirical verification of the proposed theory using artificial datasets, and
experimental comparisons with commonly used drift handling methods on
real-world datasets. The results show that the proposed theory is robust
against a range of distributions and dimensions, and the drift detection method
outperforms state-of-the-art alternative methods.
