Integrating Deep Neural Networks with Support Vector Machines for Gene Expression Classification

Gene expression classification remains a challenging task due to the high dimensionality and heterogeneity of available datasets. In this study, we present a comprehensive empirical analysis combining neural networks and Support Vector Machines (SVMs) for gene expression classification. We evaluate a wide range of architectures, including convolutional neural networks (CNNs) and the recently introduced Kolmogorov–Arnold Networks (KANs), across more than ten publicly available datasets. Furthermore, we explore ensemble strategies and show that ensemble models achieve statistically significant improvements over recently proposed state of the art approaches. All developed code, along with the exact data splits used in our 10-fold cross-validation experiments, is publicly available to ensure full reproducibility of our results.