Tabular data is prevalent across various domains in machine learning. Although Deep
Neural Network (DNN)-based methods have shown promising performance comparable to …

The growing success of deep learning in various domains has prompted investigations into
its application to tabular data, where deep models have shown promising results compared …

For classification and regression on tabular data, the dominance of gradient-boosted
decision trees (GBDTs) has recently been challenged by often much slower deep learning …

Purpose More accurate prediction of phenotype traits can increase the success of genomic
selection in both plant and animal breeding studies and provide more reliable disease risk …

Tabular data, widely used in industries like healthcare, finance, and transportation, presents
unique challenges for deep learning due to its heterogeneous nature and lack of spatial …

Deep learning architectures for supervised learning on tabular data range from simple
multilayer perceptrons (MLP) to sophisticated Transformers and retrieval-augmented …

Advances in machine learning research drive progress in real-world applications. To ensure
this progress, it is important to understand the potential pitfalls on the way from a novel …

This study explores the application of anomaly detection (AD) methods in imbalanced
learning tasks, focusing on fraud detection using real online credit card payment data. We …

Decision trees are renowned for their interpretability capability to achieve high predictive
performance, especially on tabular data. Traditionally, they are constructed through …

Tabular data is prevalent in real-world machine learning applications, and new models for
supervised learning of tabular data are frequently proposed. Comparative studies assessing …