  Traditional Machine Learning (ML) models like Support Vector Machine, Random
Forest, and Logistic Regression are generally preferred for classification
tasks on tabular datasets. Tabular data consists of rows and columns
corresponding to instances and features, respectively. Past studies indicate
that traditional classifiers often produce unsatisfactory results in complex
tabular datasets. Hence, researchers attempt to use the powerful Convolutional
Neural Networks (CNN) for tabular datasets. Recent studies propose several
techniques like SuperTML, Conditional GAN (CTGAN), and Tabular Convolution
(TAC) for applying Convolutional Neural Networks (CNN) on tabular data. These
models outperform the traditional classifiers and substantially improve the
performance on tabular data. This study introduces a novel technique, namely,
Dynamic Weighted Tabular Method (DWTM), that uses feature weights dynamically
based on statistical techniques to apply CNNs on tabular datasets. The method
assigns weights dynamically to each feature based on their strength of
associativity to the class labels. Each data point is converted into images and
fed to a CNN model. The features are allocated image canvas space based on
their weights. The DWTM is an improvement on the previously mentioned methods
as it dynamically implements the entire experimental setting rather than using
the static configuration provided in the previous methods. Furthermore, it uses
the novel idea of using feature weights to create image canvas space. In this
paper, the DWTM is applied to six benchmarked tabular datasets and it achieves
outstanding performance (i.e., average accuracy = 95%) on all of them.
