  Recently, convolution neural networks (CNNs) have attracted a great deal of
attention due to their remarkable performance in various domains, particularly
in image and text classification tasks. However, their application to tabular
data classification remains underexplored. There are many fields such as
bioinformatics, finance, medicine where nonimage data are prevalent. Adaption
of CNNs to classify nonimage data remains highly challenging. This paper
investigates the efficacy of CNNs for tabular data classification, aiming to
bridge the gap between traditional machine learning approaches and deep
learning techniques. We propose a novel framework fuzzy convolution neural
network (FCNN) tailored specifically for tabular data to capture local patterns
within feature vectors. In our approach, we map feature values to fuzzy
memberships. The fuzzy membership vectors are converted into images that are
used to train the CNN model. The trained CNN model is used to classify unknown
feature vectors. To validate our approach, we generated six complex noisy data
sets. We used randomly selected seventy percent samples from each data set for
training and thirty percent for testing. The data sets were also classified
using the state-of-the-art machine learning algorithms such as the decision
tree (DT), support vector machine (SVM), fuzzy neural network (FNN), Bayes
classifier, and Random Forest (RF). Experimental results demonstrate that our
proposed model can effectively learn meaningful representations from tabular
data, achieving competitive or superior performance compared to existing
methods. Overall, our finding suggests that the proposed FCNN model holds
promise as a viable alternative for tabular data classification tasks, offering
a fresh prospective and potentially unlocking new opportunities for leveraging
deep learning in structured data analysis.
