# A Dynamic Weighted Tabular Method for Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2205.10386 · HTML: https://mlsanity.com/paper/2205.10386/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 56 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning](https://mlsanity.com/paper/2006.00719/) (2020)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [Improving Generalization Performance by Switching from Adam to SGD](https://mlsanity.com/paper/1712.07628/) (2017)
- [Deep Image: Scaling up Image Recognition](https://mlsanity.com/paper/1501.02876/) (2015)
- [Super Characters: A Conversion from Sentiment Classification to Image Classification](https://mlsanity.com/paper/1810.07653/) (2018)
- [Text2Chart: A Multi-Staged Chart Generator from Natural Language Text](https://mlsanity.com/paper/2104.04584/) (2021)
- [A Comparative Study of Machine Learning Models for Tabular Data Through Challenge of Monitoring Parkinson's Disease Progression Using Voice Recordings](https://mlsanity.com/paper/2005.14257/) (2020)

## Built on by (most cited first, 1 of 1)

- [LM-IGTD: a 2D image generator for low-dimensional and mixed-type tabular data to leverage the potential of convolutional neural networks](https://mlsanity.com/paper/2406.14566/) (2024)

## Abstract (arXiv)

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.