  In machine learning, concept drift is an evolution of information that
invalidates the current data model. It happens when the statistical properties
of the input data change over time in unforeseen ways. Concept drift detection
is crucial when dealing with dynamically changing data. Its visualization can
bring valuable insight into the data dynamics, especially for multidimensional
data, and is related to visual knowledge discovery. We propose a novel
visualization model based on parallel coordinates, denoted as parallel
histograms through time. Our model represents histograms of feature
distributions for successive time-shifted windows. The drift is shown as
variations of these histograms, obtained by connecting the means of the
distribution for successive time windows. We show how these diagrams can be
used to explain the decision made by the machine learning model in choosing the
drift point. By isolating the drift at the edges of successive time windows,
there will be none (or reduced) drift within the adjacent windows. We
illustrate this concept on both synthetic and real datasets. In our
experiments, we use an incremental/decremental SVM with shifting window,
introduced by us in previous work. With our proposed technique, in addition to
detect the presence of concept drift, we can also depict it. This information
can be further used to explain the change. mental results, opening the
possibility for further investigations.
