Vortex Feature Positioning: Bridging Tabular IIoT Data and Image-Based Deep Learning
This addresses inefficiencies and overfitting in tabular-to-image conversion for IIoT data analysis, representing an incremental improvement in domain-specific deep learning applications.
The paper tackled the problem of converting tabular IIoT data into images for deep learning, which often leads to overfitting and inefficiency due to fixed image sizes, by introducing Vortex Feature Positioning (VFP) that arranges features based on correlation in a vortex pattern, resulting in outperformance over traditional methods and existing conversion techniques across seven datasets.
Tabular data from IIoT devices are typically analyzed using decision tree-based machine learning techniques, which struggle with high-dimensional and numeric data. To overcome these limitations, techniques converting tabular data into images have been developed, leveraging the strengths of image-based deep learning approaches such as Convolutional Neural Networks. These methods cluster similar features into distinct image areas with fixed sizes, regardless of the number of features, resembling actual photographs. However, this increases the possibility of overfitting, as similar features, when selected carefully in a tabular format, are often discarded to prevent this issue. Additionally, fixed image sizes can lead to wasted pixels with fewer features, resulting in computational inefficiency. We introduce Vortex Feature Positioning (VFP) to address these issues. VFP arranges features based on their correlation, spacing similar ones in a vortex pattern from the image center, with the image size determined by the attribute count. VFP outperforms traditional machine learning methods and existing conversion techniques in tests across seven datasets with varying real-valued attributes.