VFP: Converting Tabular Data for IIoT into Images Considering Correlations of Attributes for Convolutional Neural Networks
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For tabular data generated from IIoT devices, traditional machine learning (ML) techniques based on the decision tree algorithm have been employed. However, these methods have limitations in processing tabular data where real number attributes dominate. To address this issue, DeepInsight, REFINED, and IGTD were proposed to convert tabular data into images for utilizing convolutional neural networks (CNNs). They gather similar features in some specific spots of an image to make the converted image look like an actual image. Gathering similar features contrasts with traditional ML techniques for tabular data, which drops some highly correlated attributes to avoid overfitting. Also, previous converting methods fixed the image size, and there are wasted or insufficient pixels according to the number of attributes of tabular data. Therefore, this paper proposes a new converting method, Vortex Feature Positioning (VFP). VFP considers the correlation of features and places similar features far away from each. Features are positioned in the vortex shape from the center of an image, and the number of attributes determines the image size. VFP shows better test performance than traditional ML techniques for tabular data and previous converting methods in five datasets: Iris, Wine, Dry Bean, Epileptic Seizure, and SECOM, which have differences in the number of attributes.
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