Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification

TL;DR

This paper introduces SSTML, a novel method that transforms difficult data streams into images using multi-dimensional encoding and applies ResNet-18 for classification, outperforming existing algorithms in accuracy.

Contribution

It pioneers the use of multi-dimensional encoding for data stream classification, integrating image-based deep learning with a new encoding algorithm for improved performance.

Findings

SSTML achieves statistically significant better accuracy than state-of-the-art methods.

SSTML maintains comparable processing times to existing algorithms.

The approach effectively handles concept drift and data imbalance in streams.

Abstract

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.