A Dynamic Weighted Tabular Method for Convolutional Neural Networks

TL;DR

This paper introduces the Dynamic Weighted Tabular Method (DWTM), a novel approach that applies CNNs to tabular data by dynamically assigning feature weights and converting data into images, resulting in high classification accuracy.

Contribution

The study presents a new dynamic weighting technique for features in CNN-based tabular data classification, improving over static methods by adapting feature importance during training.

Findings

Achieved an average accuracy of 95% on six benchmark datasets.

Outperformed existing static CNN-based methods in tabular data classification.

Demonstrated the effectiveness of dynamic feature weighting in CNN applications.

Abstract

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.