  Rapid technological advances are inherently linked to the increased amount of
data, a substantial portion of which can be interpreted as data stream, capable
of exhibiting the phenomenon of concept drift and having a high imbalance
ratio. Consequently, developing new approaches to classifying difficult data
streams is a rapidly growing research area. At the same time, the proliferation
of deep learning and transfer learning, as well as the success of convolutional
neural networks in computer vision tasks, have contributed to the emergence of
a new research trend, namely Multi-Dimensional Encoding (MDE), focusing on
transforming tabular data into a homogeneous form of a discrete digital signal.
This paper proposes Streaming Super Tabular Machine Learning (SSTML), thereby
exploring for the first time the potential of MDE in the difficult data stream
classification task. SSTML encodes consecutive data chunks into an image
representation using the STML algorithm and then performs a single ResNet-18
training epoch. Experiments conducted on synthetic and real data streams have
demonstrated the ability of SSTML to achieve classification quality
statistically significantly superior to state-of-the-art algorithms while
maintaining comparable processing time.
