LAUSR

Traditional Machine Learning (ML) models are generally preferred for classification tasks on tabular datasets, which often produce unsatisfactory results in complex tabular datasets. Recent works, using Convolutional Neural Networks (CNN) with embedding techniques, outperform the traditional classifiers on tabular dataset. However, these embedding techniques fail to use an automated approach after analyzing the importance of the features in the dataset accurately. This study introduces a novel feature embedding technique named Dynamic Weighted Tabular Method (DWTM), which dynamically uses feature weights based on their strength of the correlations to the class labels during applying any CNN architectures on the tabular datasets. DWTM converts each data point into images and then feeds to a CNN architecture. It dynamically embeds the features of the tabular dataset based on their strength and assigns pixel positions to the appropriate features in the image canvas space instead of using any static configuration. In this paper, DWTM embedding method is applied over six benchmark tabular datasets independently by using three different CNN architectures (i.e., ResNet-18, DenseNet and InceptionV1) and an outstanding performance (an average accuracy of 98%) has obtained, which outperforms any traditional and CNN based classifiers as well.