A Novel 2D-CNN Framework for Tabular Data Classification Based on Multi-Channel Fuzzy Clustering

Abstract

Converting non-visual numerical matrices into artificial images has emerged as a major paradigm shift, enabling deep networks to leverage spatial inductive biases. However, single-layer transformations often fail to capture complex non-linear transitions, localized spatial associations, and multi-layered data structures. To overcome these challenges, this study introduces a novel, order-invariant tabular-to-image mapping framework that systematically constructs information-dense 4D artificial image tensors. The proposed architecture dynamically integrates normalized real features with cell-based Fuzzy C-Means memberships (m=2.0), Gustafson-Kessel elliptic variance partitions (m=1.5), and column-wise Possibilistic Fuzzy C-Means typicality indicators. Evaluated over ten diversified UCI benchmark datasets using a rigorous 10-fold stratified cross-validation pipeline, the framework successfully achieves a global Mean Rank of 3.50. Strikingly, despite being executed over a highly restricted, lightweight hyperparameter search grid via Random Search, no statistically significant difference was detected between the proposed model and top-tier, extensively optimized baselines like Support Vector Machines and Multi-Layer Perceptrons. Friedman test (χ^2=42.961, P<0.001) and post-hoc Wilcoxon tests with Holm-Bonferroni corrections confirm a statistically significant superiority over conventional classifiers like Decision Trees (Holm" " P=0.018), providing a highly scalable alternative for high-accuracy numerical data mining.

Keywords

• Tabular-to-image conversion
• Classification
• Fuzzy clustering
• Convolutional neural networks
• Feature matrix

Ethical Statement

Ethics committee approval was not required for this study because of there was no study on animals or humans.

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